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<div class="title">TensorContraction.h</div>  </div>
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<div class="contents">
<div class="fragment"><div class="line"><a name="l00001"></a><span class="lineno">    1</span>&#160;<span class="comment">// This file is part of Eigen, a lightweight C++ template library</span></div>
<div class="line"><a name="l00002"></a><span class="lineno">    2</span>&#160;<span class="comment">// for linear algebra.</span></div>
<div class="line"><a name="l00003"></a><span class="lineno">    3</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00004"></a><span class="lineno">    4</span>&#160;<span class="comment">// Copyright (C) 2014 Benoit Steiner &lt;benoit.steiner.goog@gmail.com&gt;</span></div>
<div class="line"><a name="l00005"></a><span class="lineno">    5</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00006"></a><span class="lineno">    6</span>&#160;<span class="comment">// This Source Code Form is subject to the terms of the Mozilla</span></div>
<div class="line"><a name="l00007"></a><span class="lineno">    7</span>&#160;<span class="comment">// Public License v. 2.0. If a copy of the MPL was not distributed</span></div>
<div class="line"><a name="l00008"></a><span class="lineno">    8</span>&#160;<span class="comment">// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.</span></div>
<div class="line"><a name="l00009"></a><span class="lineno">    9</span>&#160; </div>
<div class="line"><a name="l00010"></a><span class="lineno">   10</span>&#160;<span class="preprocessor">#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H</span></div>
<div class="line"><a name="l00011"></a><span class="lineno">   11</span>&#160;<span class="preprocessor">#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H</span></div>
<div class="line"><a name="l00012"></a><span class="lineno">   12</span>&#160; </div>
<div class="line"><a name="l00013"></a><span class="lineno">   13</span>&#160;<span class="preprocessor">#include &quot;./InternalHeaderCheck.h&quot;</span></div>
<div class="line"><a name="l00014"></a><span class="lineno">   14</span>&#160; </div>
<div class="line"><a name="l00015"></a><span class="lineno">   15</span>&#160;<span class="keyword">namespace </span><a class="code" href="namespaceEigen.html">Eigen</a> {</div>
<div class="line"><a name="l00016"></a><span class="lineno">   16</span>&#160; </div>
<div class="line"><a name="l00024"></a><span class="lineno">   24</span>&#160;<span class="keyword">namespace </span>internal {</div>
<div class="line"><a name="l00025"></a><span class="lineno">   25</span>&#160; </div>
<div class="line"><a name="l00026"></a><span class="lineno">   26</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Dimensions, <span class="keyword">typename</span> LhsXprType, <span class="keyword">typename</span> RhsXprType, <span class="keyword">typename</span> OutputKernelType&gt;</div>
<div class="line"><a name="l00027"></a><span class="lineno">   27</span>&#160;<span class="keyword">struct </span>traits&lt;TensorContractionOp&lt;Dimensions, LhsXprType, RhsXprType, OutputKernelType&gt; &gt;</div>
<div class="line"><a name="l00028"></a><span class="lineno">   28</span>&#160;{</div>
<div class="line"><a name="l00029"></a><span class="lineno">   29</span>&#160;  <span class="comment">// Type promotion to handle the case where the types of the lhs and the rhs are different.</span></div>
<div class="line"><a name="l00030"></a><span class="lineno">   30</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> gebp_traits&lt;std::remove_const_t&lt;typename LhsXprType::Scalar&gt;,</div>
<div class="line"><a name="l00031"></a><span class="lineno">   31</span>&#160;                               std::remove_const_t&lt;typename RhsXprType::Scalar&gt;&gt;::ResScalar Scalar;</div>
<div class="line"><a name="l00032"></a><span class="lineno">   32</span>&#160; </div>
<div class="line"><a name="l00033"></a><span class="lineno">   33</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> promote_storage_type&lt;typename traits&lt;LhsXprType&gt;::StorageKind,</div>
<div class="line"><a name="l00034"></a><span class="lineno">   34</span>&#160;                                        <span class="keyword">typename</span> traits&lt;RhsXprType&gt;::StorageKind&gt;::ret StorageKind;</div>
<div class="line"><a name="l00035"></a><span class="lineno">   35</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> promote_index_type&lt;typename traits&lt;LhsXprType&gt;::Index,</div>
<div class="line"><a name="l00036"></a><span class="lineno">   36</span>&#160;                                      <span class="keyword">typename</span> traits&lt;RhsXprType&gt;::Index&gt;::type <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00037"></a><span class="lineno">   37</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> LhsXprType::Nested LhsNested;</div>
<div class="line"><a name="l00038"></a><span class="lineno">   38</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> RhsXprType::Nested RhsNested;</div>
<div class="line"><a name="l00039"></a><span class="lineno">   39</span>&#160;  <span class="keyword">typedef</span> std::remove_reference_t&lt;LhsNested&gt; LhsNested_;</div>
<div class="line"><a name="l00040"></a><span class="lineno">   40</span>&#160;  <span class="keyword">typedef</span> std::remove_reference_t&lt;RhsNested&gt; RhsNested_;</div>
<div class="line"><a name="l00041"></a><span class="lineno">   41</span>&#160; </div>
<div class="line"><a name="l00042"></a><span class="lineno">   42</span>&#160;  <span class="comment">// From NumDims below.</span></div>
<div class="line"><a name="l00043"></a><span class="lineno">   43</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDimensions = traits&lt;LhsXprType&gt;::NumDimensions + traits&lt;RhsXprType&gt;::NumDimensions - 2 * array_size&lt;Dimensions&gt;::value;</div>
<div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> Layout = traits&lt;LhsXprType&gt;::Layout;</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;Pointer_type_promotion&lt;typename LhsXprType::Scalar, Scalar&gt;::val,</div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;                        <span class="keyword">typename</span> traits&lt;LhsXprType&gt;::PointerType,</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;                        <span class="keyword">typename</span> traits&lt;RhsXprType&gt;::PointerType&gt;</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;      PointerType;</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160; </div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;    Flags = 0</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160;  };</div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;};</div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160; </div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Dimensions, <span class="keyword">typename</span> LhsXprType, <span class="keyword">typename</span> RhsXprType, <span class="keyword">typename</span> OutputKernelType&gt;</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160;<span class="keyword">struct </span>eval&lt;TensorContractionOp&lt;Dimensions, LhsXprType, RhsXprType, OutputKernelType&gt;, <a class="code" href="namespaceEigen.html">Eigen</a>::Dense&gt;</div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;{</div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">const</span> TensorContractionOp&lt;Dimensions, LhsXprType, RhsXprType, OutputKernelType&gt;&amp; type;</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;};</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160; </div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Dimensions, <span class="keyword">typename</span> LhsXprType, <span class="keyword">typename</span> RhsXprType, <span class="keyword">typename</span> OutputKernelType&gt;</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;<span class="keyword">struct </span>nested&lt;TensorContractionOp&lt;Dimensions, LhsXprType, RhsXprType, OutputKernelType&gt;, 1, typename eval&lt;TensorContractionOp&lt;Dimensions, LhsXprType, RhsXprType, OutputKernelType&gt; &gt;::type&gt;</div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;{</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  <span class="keyword">typedef</span> TensorContractionOp&lt;Dimensions, LhsXprType, RhsXprType, OutputKernelType&gt; type;</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;};</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Indices_, <span class="keyword">typename</span> LeftArgType_, <span class="keyword">typename</span> RightArgType_, <span class="keyword">typename</span> OutputKernelType_, <span class="keyword">typename</span> Device_&gt;</div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;<span class="keyword">struct </span>traits&lt;TensorEvaluator&lt;const TensorContractionOp&lt;Indices_, LeftArgType_, RightArgType_, OutputKernelType_&gt;, Device_&gt; &gt; {</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  <span class="keyword">typedef</span> Indices_ Indices;</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160;  <span class="keyword">typedef</span> LeftArgType_ LeftArgType;</div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;  <span class="keyword">typedef</span> RightArgType_ RightArgType;</div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  <span class="keyword">typedef</span> OutputKernelType_ OutputKernelType;</div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;  <span class="keyword">typedef</span> Device_ Device;</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160; </div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;  <span class="comment">// From NumDims below.</span></div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDimensions = traits&lt;LeftArgType_&gt;::NumDimensions + traits&lt;RightArgType_&gt;::NumDimensions - 2 * array_size&lt;Indices_&gt;::value;</div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;};</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160; </div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;<span class="comment">// Helper class to allocate and deallocate temporary memory for packed buffers.</span></div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> LhsScalar, <span class="keyword">typename</span> RhsScalar&gt;</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160;<span class="keyword">struct </span>TensorContractionBlockMemAllocator {</div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;  <span class="keyword">typedef</span> <span class="keywordtype">void</span>* BlockMemHandle;</div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160; </div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> BlockMemHandle allocate(Device&amp; d, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bm,</div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;                                                   <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bk,</div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;                                                   <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bn,</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;                                                   LhsScalar** lhs_block,</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160;                                                   RhsScalar** rhs_block) {</div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    eigen_assert(lhs_block);</div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    eigen_assert(rhs_block);</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;    <span class="keywordtype">char</span>* block_mem = <span class="keyword">static_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span>(d.allocate(sz.lhs_size + sz.rhs_size));</div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;    eigen_assert(block_mem);</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;    *lhs_block = <span class="keyword">reinterpret_cast&lt;</span>LhsScalar*<span class="keyword">&gt;</span>(block_mem);</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;    *rhs_block = <span class="keyword">reinterpret_cast&lt;</span>RhsScalar*<span class="keyword">&gt;</span>(block_mem + sz.lhs_size);</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;    <span class="keywordflow">return</span> block_mem;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;  }</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160; </div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> BlockMemHandle allocateSlices(</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;      Device&amp; d, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bm, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bk, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bn,</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> num_lhs, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> num_rhs, <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> num_slices,</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;      std::vector&lt;LhsScalar*&gt;* lhs_blocks,</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;      std::vector&lt;RhsScalar*&gt;* rhs_blocks) {</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;    eigen_assert(num_slices &gt; 0);</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;    eigen_assert(num_lhs &gt;= 0 &amp;&amp; num_rhs &gt;= 0);</div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;    eigen_assert(num_lhs == 0 || lhs_blocks);</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;    eigen_assert(num_rhs == 0 || rhs_blocks);</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;    BlockSizes sz = ComputeLhsRhsBlockSizes(bm, bk, bn);</div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;    <span class="keywordtype">void</span>* block_mem = d.allocate(</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;        (num_lhs * sz.lhs_size + num_rhs * sz.rhs_size) * num_slices);</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;    eigen_assert(block_mem);</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;    <span class="keywordtype">char</span>* mem = <span class="keyword">static_cast&lt;</span><span class="keywordtype">char</span>*<span class="keyword">&gt;</span>(block_mem);</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160; </div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> x = 0; x &lt; num_slices; x++) {</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160;      <span class="keywordflow">if</span> (num_lhs &gt; 0) lhs_blocks[x].resize(num_lhs);</div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;      <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> m = 0; m &lt; num_lhs; m++) {</div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;        lhs_blocks[x][m] = <span class="keyword">reinterpret_cast&lt;</span>LhsScalar*<span class="keyword">&gt;</span>(mem);</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        mem += sz.lhs_size;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;      }</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;      <span class="keywordflow">if</span> (num_rhs &gt; 0) rhs_blocks[x].resize(num_rhs);</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;      <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> n = 0; n &lt; num_rhs; n++) {</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;        rhs_blocks[x][n] = <span class="keyword">reinterpret_cast&lt;</span>RhsScalar*<span class="keyword">&gt;</span>(mem);</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;        mem += sz.rhs_size;</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      }</div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;    }</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;    <span class="keywordflow">return</span> block_mem;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;  }</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160; </div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> <span class="keywordtype">void</span> deallocate(Device&amp; d, BlockMemHandle handle) {</div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;    d.deallocate(handle);</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;  }</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160; </div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  <span class="keyword">struct </span>BlockSizes {</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lhs_size;</div>
<div class="line"><a name="l00140"></a><span class="lineno">  140</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> rhs_size;</div>
<div class="line"><a name="l00141"></a><span class="lineno">  141</span>&#160;  };</div>
<div class="line"><a name="l00142"></a><span class="lineno">  142</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> BlockSizes ComputeLhsRhsBlockSizes(<span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bm,</div>
<div class="line"><a name="l00143"></a><span class="lineno">  143</span>&#160;                                                              <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bk,</div>
<div class="line"><a name="l00144"></a><span class="lineno">  144</span>&#160;                                                              <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> bn) {</div>
<div class="line"><a name="l00145"></a><span class="lineno">  145</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> align = numext::maxi(EIGEN_MAX_ALIGN_BYTES, 1);</div>
<div class="line"><a name="l00146"></a><span class="lineno">  146</span>&#160;    BlockSizes sz;</div>
<div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;    sz.lhs_size = divup&lt;Index&gt;(bm * bk * <span class="keyword">sizeof</span>(LhsScalar), align) * align;</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;    sz.rhs_size = divup&lt;Index&gt;(bn * bk * <span class="keyword">sizeof</span>(RhsScalar), align) * align;</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;    <span class="keywordflow">return</span> sz;</div>
<div class="line"><a name="l00150"></a><span class="lineno">  150</span>&#160;  }</div>
<div class="line"><a name="l00151"></a><span class="lineno">  151</span>&#160;};</div>
<div class="line"><a name="l00152"></a><span class="lineno">  152</span>&#160; </div>
<div class="line"><a name="l00153"></a><span class="lineno">  153</span>&#160;<span class="comment">// WARNING: In this code we assume that Lhs and Rhs tensor expressions are in</span></div>
<div class="line"><a name="l00154"></a><span class="lineno">  154</span>&#160;<span class="comment">// ColMajor storage order. This property is guaranteed by the</span></div>
<div class="line"><a name="l00155"></a><span class="lineno">  155</span>&#160;<span class="comment">// TensorContractionOp evaluator. TensorContractionKernel specifies how we pack</span></div>
<div class="line"><a name="l00156"></a><span class="lineno">  156</span>&#160;<span class="comment">// blocks of Lhs and Rhs tensor expressions, and how we invoke matrix</span></div>
<div class="line"><a name="l00157"></a><span class="lineno">  157</span>&#160;<span class="comment">// multiplication for these blocks. Default tensor contraction uses</span></div>
<div class="line"><a name="l00158"></a><span class="lineno">  158</span>&#160;<span class="comment">// gemm_pack_rhs, gemm_pack_lhs and gebp_kernel from Eigen Core (see</span></div>
<div class="line"><a name="l00159"></a><span class="lineno">  159</span>&#160;<span class="comment">// GeneralBlocPanelKernel.h for details).</span></div>
<div class="line"><a name="l00160"></a><span class="lineno">  160</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00161"></a><span class="lineno">  161</span>&#160;<span class="comment">// By specializing contraction kernels we can use other low level libraries to</span></div>
<div class="line"><a name="l00162"></a><span class="lineno">  162</span>&#160;<span class="comment">// perform matrix multiplication, and still rely on Eigen contraction evaluator.</span></div>
<div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;<span class="comment">// This also includes full support in TensorContractionThreadPool, assuming that</span></div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;<span class="comment">// underlying gemm do not use it&#39;s own threading.</span></div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00166"></a><span class="lineno">  166</span>&#160;<span class="comment">// - ResScalar/LhsScalar/RhsScalar - scalar type for the result of</span></div>
<div class="line"><a name="l00167"></a><span class="lineno">  167</span>&#160;<span class="comment">//   multiplication, lhs tensor and rhs tensor respectively.</span></div>
<div class="line"><a name="l00168"></a><span class="lineno">  168</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00169"></a><span class="lineno">  169</span>&#160;<span class="comment">// - StorageIndex - index type for the tensor expressions. In practice almost</span></div>
<div class="line"><a name="l00170"></a><span class="lineno">  170</span>&#160;<span class="comment">//   always is Eigen::Index.</span></div>
<div class="line"><a name="l00171"></a><span class="lineno">  171</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00172"></a><span class="lineno">  172</span>&#160;<span class="comment">// - OutputMapper provides access to the memory of the output matrix. In</span></div>
<div class="line"><a name="l00173"></a><span class="lineno">  173</span>&#160;<span class="comment">//   practice it&#39;s always column major blas_data_mapper (it must be of ResScalar</span></div>
<div class="line"><a name="l00174"></a><span class="lineno">  174</span>&#160;<span class="comment">//   type).</span></div>
<div class="line"><a name="l00175"></a><span class="lineno">  175</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00176"></a><span class="lineno">  176</span>&#160;<span class="comment">// - LhsMapper/RhsMapper similarly to blas_data_mapper provide a two dimensional</span></div>
<div class="line"><a name="l00177"></a><span class="lineno">  177</span>&#160;<span class="comment">//   view into the Lhs/Rhs tensor expressions. In practice it&#39;s</span></div>
<div class="line"><a name="l00178"></a><span class="lineno">  178</span>&#160;<span class="comment">//   TensorContractionInputMapper, or some specialization of it based on the</span></div>
<div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;<span class="comment">//   type of tensor expression (e.g. TensorImagePatchOp has optimized input</span></div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;<span class="comment">//   mapper).</span></div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;<span class="keyword">template</span> &lt;<span class="keyword">typename</span> ResScalar, <span class="keyword">typename</span> LhsScalar, <span class="keyword">typename</span> RhsScalar,</div>
<div class="line"><a name="l00182"></a><span class="lineno">  182</span>&#160;    <span class="keyword">typename</span> StorageIndex, <span class="keyword">typename</span> OutputMapper, <span class="keyword">typename</span> LhsMapper,</div>
<div class="line"><a name="l00183"></a><span class="lineno">  183</span>&#160;    <span class="keyword">typename</span> RhsMapper&gt;</div>
<div class="line"><a name="l00184"></a><span class="lineno">  184</span>&#160;<span class="keyword">struct </span>TensorContractionKernel {</div>
<div class="line"><a name="l00185"></a><span class="lineno">  185</span>&#160;  <span class="comment">// True if `invoke()` supports `beta` in `C &lt;- alpha * A * B + beta * C`</span></div>
<div class="line"><a name="l00186"></a><span class="lineno">  186</span>&#160;  <span class="comment">// (otherwise beta should be always equal to 1).</span></div>
<div class="line"><a name="l00187"></a><span class="lineno">  187</span>&#160;  <span class="keyword">enum</span> { HasBeta = <span class="keyword">false</span> };</div>
<div class="line"><a name="l00188"></a><span class="lineno">  188</span>&#160; </div>
<div class="line"><a name="l00189"></a><span class="lineno">  189</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00190"></a><span class="lineno">  190</span>&#160;  TensorContractionKernel(StorageIndex m_, StorageIndex k_, StorageIndex n_,</div>
<div class="line"><a name="l00191"></a><span class="lineno">  191</span>&#160;                          StorageIndex bm_, StorageIndex bk_, StorageIndex bn_)</div>
<div class="line"><a name="l00192"></a><span class="lineno">  192</span>&#160;      : m(m_), k(k_), n(n_), bm(bm_), bk(bk_), bn(bn_) {}</div>
<div class="line"><a name="l00193"></a><span class="lineno">  193</span>&#160; </div>
<div class="line"><a name="l00194"></a><span class="lineno">  194</span>&#160;  <span class="comment">// Pack blocks of Lhs and Rhs into contiguous blocks in memory.</span></div>
<div class="line"><a name="l00195"></a><span class="lineno">  195</span>&#160;  <span class="keyword">typedef</span> LhsScalar* LhsBlock;</div>
<div class="line"><a name="l00196"></a><span class="lineno">  196</span>&#160;  <span class="keyword">typedef</span> RhsScalar* RhsBlock;</div>
<div class="line"><a name="l00197"></a><span class="lineno">  197</span>&#160; </div>
<div class="line"><a name="l00198"></a><span class="lineno">  198</span>&#160;  <span class="comment">// Packed Lhs/Rhs block memory allocator.</span></div>
<div class="line"><a name="l00199"></a><span class="lineno">  199</span>&#160;  <span class="keyword">typedef</span> TensorContractionBlockMemAllocator&lt;LhsScalar, RhsScalar&gt;</div>
<div class="line"><a name="l00200"></a><span class="lineno">  200</span>&#160;      BlockMemAllocator;</div>
<div class="line"><a name="l00201"></a><span class="lineno">  201</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> BlockMemAllocator::BlockMemHandle BlockMemHandle;</div>
<div class="line"><a name="l00202"></a><span class="lineno">  202</span>&#160; </div>
<div class="line"><a name="l00203"></a><span class="lineno">  203</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::gebp_traits&lt;LhsScalar, RhsScalar&gt; Traits;</div>
<div class="line"><a name="l00204"></a><span class="lineno">  204</span>&#160; </div>
<div class="line"><a name="l00205"></a><span class="lineno">  205</span>&#160;  <span class="keyword">typedef</span> internal::gemm_pack_lhs&lt;</div>
<div class="line"><a name="l00206"></a><span class="lineno">  206</span>&#160;      LhsScalar, StorageIndex, <span class="keyword">typename</span> LhsMapper::SubMapper, Traits::mr,</div>
<div class="line"><a name="l00207"></a><span class="lineno">  207</span>&#160;      Traits::LhsProgress, <span class="keyword">typename</span> Traits::LhsPacket4Packing, <a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>&gt;</div>
<div class="line"><a name="l00208"></a><span class="lineno">  208</span>&#160;      LhsPacker;</div>
<div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160; </div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;  <span class="keyword">typedef</span> internal::gemm_pack_rhs&lt;RhsScalar, StorageIndex,</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;                                  <span class="keyword">typename</span> RhsMapper::SubMapper, Traits::nr,</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;                                  <a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>&gt;</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;      RhsPacker;</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160; </div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;  <span class="keyword">typedef</span> internal::gebp_kernel&lt;LhsScalar, RhsScalar, StorageIndex,</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;                                OutputMapper, Traits::mr, Traits::nr,</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;      <span class="comment">/*ConjugateLhs*/</span> <span class="keyword">false</span>, <span class="comment">/*ConjugateRhs*/</span> <span class="keyword">false</span>&gt;</div>
<div class="line"><a name="l00218"></a><span class="lineno">  218</span>&#160;      GebpKernel;</div>
<div class="line"><a name="l00219"></a><span class="lineno">  219</span>&#160; </div>
<div class="line"><a name="l00220"></a><span class="lineno">  220</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00221"></a><span class="lineno">  221</span>&#160;  EIGEN_DEVICE_FUNC BlockMemHandle allocate(Device&amp; d, LhsBlock* lhs_block,</div>
<div class="line"><a name="l00222"></a><span class="lineno">  222</span>&#160;                                            RhsBlock* rhs_block) {</div>
<div class="line"><a name="l00223"></a><span class="lineno">  223</span>&#160;    <span class="keywordflow">return</span> BlockMemAllocator::allocate(d, bm, bk, bn, lhs_block, rhs_block);</div>
<div class="line"><a name="l00224"></a><span class="lineno">  224</span>&#160;  }</div>
<div class="line"><a name="l00225"></a><span class="lineno">  225</span>&#160; </div>
<div class="line"><a name="l00226"></a><span class="lineno">  226</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00227"></a><span class="lineno">  227</span>&#160;  EIGEN_DEVICE_FUNC BlockMemHandle allocateSlices(</div>
<div class="line"><a name="l00228"></a><span class="lineno">  228</span>&#160;      Device&amp; d, <span class="keyword">const</span> StorageIndex num_lhs, <span class="keyword">const</span> StorageIndex num_rhs,</div>
<div class="line"><a name="l00229"></a><span class="lineno">  229</span>&#160;      <span class="keyword">const</span> StorageIndex num_slices, std::vector&lt;LhsBlock&gt;* lhs_blocks,</div>
<div class="line"><a name="l00230"></a><span class="lineno">  230</span>&#160;      std::vector&lt;RhsBlock&gt;* rhs_blocks) {</div>
<div class="line"><a name="l00231"></a><span class="lineno">  231</span>&#160;    <span class="keywordflow">return</span> BlockMemAllocator::allocateSlices(</div>
<div class="line"><a name="l00232"></a><span class="lineno">  232</span>&#160;        d, bm, bk, bn, num_lhs, num_rhs, num_slices, lhs_blocks, rhs_blocks);</div>
<div class="line"><a name="l00233"></a><span class="lineno">  233</span>&#160;  }</div>
<div class="line"><a name="l00234"></a><span class="lineno">  234</span>&#160; </div>
<div class="line"><a name="l00235"></a><span class="lineno">  235</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00236"></a><span class="lineno">  236</span>&#160;  EIGEN_DEVICE_FUNC <span class="keyword">static</span> <span class="keywordtype">void</span> deallocate(Device&amp; d, BlockMemHandle handle) {</div>
<div class="line"><a name="l00237"></a><span class="lineno">  237</span>&#160;    BlockMemAllocator::deallocate(d, handle);</div>
<div class="line"><a name="l00238"></a><span class="lineno">  238</span>&#160;  }</div>
<div class="line"><a name="l00239"></a><span class="lineno">  239</span>&#160; </div>
<div class="line"><a name="l00240"></a><span class="lineno">  240</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE <span class="keywordtype">void</span> packLhs(</div>
<div class="line"><a name="l00241"></a><span class="lineno">  241</span>&#160;      LhsBlock* lhsBlock, <span class="keyword">const</span> <span class="keyword">typename</span> LhsMapper::SubMapper&amp; data_mapper,</div>
<div class="line"><a name="l00242"></a><span class="lineno">  242</span>&#160;      <span class="keyword">const</span> StorageIndex depth, <span class="keyword">const</span> StorageIndex rows) {</div>
<div class="line"><a name="l00243"></a><span class="lineno">  243</span>&#160;    LhsPacker()(*lhsBlock, data_mapper, depth, rows, <span class="comment">/*stride*/</span> 0,</div>
<div class="line"><a name="l00244"></a><span class="lineno">  244</span>&#160;        <span class="comment">/*offset*/</span> 0);</div>
<div class="line"><a name="l00245"></a><span class="lineno">  245</span>&#160;  }</div>
<div class="line"><a name="l00246"></a><span class="lineno">  246</span>&#160; </div>
<div class="line"><a name="l00247"></a><span class="lineno">  247</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE <span class="keywordtype">void</span> packRhs(</div>
<div class="line"><a name="l00248"></a><span class="lineno">  248</span>&#160;      RhsBlock* rhsBlock, <span class="keyword">const</span> <span class="keyword">typename</span> RhsMapper::SubMapper&amp; data_mapper,</div>
<div class="line"><a name="l00249"></a><span class="lineno">  249</span>&#160;      <span class="keyword">const</span> StorageIndex depth, <span class="keyword">const</span> StorageIndex cols) {</div>
<div class="line"><a name="l00250"></a><span class="lineno">  250</span>&#160;    RhsPacker()(*rhsBlock, data_mapper, depth, cols);</div>
<div class="line"><a name="l00251"></a><span class="lineno">  251</span>&#160;  }</div>
<div class="line"><a name="l00252"></a><span class="lineno">  252</span>&#160; </div>
<div class="line"><a name="l00253"></a><span class="lineno">  253</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_DONT_INLINE <span class="keywordtype">void</span> invoke(</div>
<div class="line"><a name="l00254"></a><span class="lineno">  254</span>&#160;      <span class="keyword">const</span> OutputMapper&amp; output_mapper, <span class="keyword">const</span> LhsBlock&amp; lhsBlock,</div>
<div class="line"><a name="l00255"></a><span class="lineno">  255</span>&#160;      <span class="keyword">const</span> RhsBlock&amp; rhsBlock, <span class="keyword">const</span> StorageIndex rows,</div>
<div class="line"><a name="l00256"></a><span class="lineno">  256</span>&#160;      <span class="keyword">const</span> StorageIndex depth, <span class="keyword">const</span> StorageIndex cols,</div>
<div class="line"><a name="l00257"></a><span class="lineno">  257</span>&#160;      <span class="keyword">const</span> ResScalar alpha, <span class="keyword">const</span> ResScalar beta) {</div>
<div class="line"><a name="l00258"></a><span class="lineno">  258</span>&#160;    <span class="comment">// Default GEBP kernel does not support beta.</span></div>
<div class="line"><a name="l00259"></a><span class="lineno">  259</span>&#160;    eigen_assert(beta == ResScalar(1));</div>
<div class="line"><a name="l00260"></a><span class="lineno">  260</span>&#160;    <span class="keyword">static</span> <span class="keyword">const</span> <span class="keywordtype">int</span> kComputeStrideFromBlockDimensions = -1;</div>
<div class="line"><a name="l00261"></a><span class="lineno">  261</span>&#160;    GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,</div>
<div class="line"><a name="l00262"></a><span class="lineno">  262</span>&#160;        <span class="comment">/*strideA*/</span> kComputeStrideFromBlockDimensions,</div>
<div class="line"><a name="l00263"></a><span class="lineno">  263</span>&#160;        <span class="comment">/*strideB*/</span> kComputeStrideFromBlockDimensions,</div>
<div class="line"><a name="l00264"></a><span class="lineno">  264</span>&#160;        <span class="comment">/*offsetA*/</span> 0, <span class="comment">/*offsetB*/</span> 0);</div>
<div class="line"><a name="l00265"></a><span class="lineno">  265</span>&#160;  }</div>
<div class="line"><a name="l00266"></a><span class="lineno">  266</span>&#160; </div>
<div class="line"><a name="l00267"></a><span class="lineno">  267</span>&#160; <span class="keyword">private</span>:</div>
<div class="line"><a name="l00268"></a><span class="lineno">  268</span>&#160;  <span class="comment">// These are dimensions of the original Tensors, and selected block sizes. The</span></div>
<div class="line"><a name="l00269"></a><span class="lineno">  269</span>&#160;  <span class="comment">// actual block sizes passed to all function above might be smaller because of</span></div>
<div class="line"><a name="l00270"></a><span class="lineno">  270</span>&#160;  <span class="comment">// the partial blocks at the end.</span></div>
<div class="line"><a name="l00271"></a><span class="lineno">  271</span>&#160;  <span class="keyword">const</span> StorageIndex m;</div>
<div class="line"><a name="l00272"></a><span class="lineno">  272</span>&#160;  <span class="keyword">const</span> StorageIndex k;</div>
<div class="line"><a name="l00273"></a><span class="lineno">  273</span>&#160;  <span class="keyword">const</span> StorageIndex n;</div>
<div class="line"><a name="l00274"></a><span class="lineno">  274</span>&#160;  <span class="keyword">const</span> StorageIndex bm;</div>
<div class="line"><a name="l00275"></a><span class="lineno">  275</span>&#160;  <span class="keyword">const</span> StorageIndex bk;</div>
<div class="line"><a name="l00276"></a><span class="lineno">  276</span>&#160;  <span class="keyword">const</span> StorageIndex bn;</div>
<div class="line"><a name="l00277"></a><span class="lineno">  277</span>&#160;};</div>
<div class="line"><a name="l00278"></a><span class="lineno">  278</span>&#160; </div>
<div class="line"><a name="l00279"></a><span class="lineno">  279</span>&#160;}  <span class="comment">// end namespace internal</span></div>
<div class="line"><a name="l00280"></a><span class="lineno">  280</span>&#160; </div>
<div class="line"><a name="l00281"></a><span class="lineno">  281</span>&#160;<span class="comment">// Tensor contraction params that should enable to get from output matrix</span></div>
<div class="line"><a name="l00282"></a><span class="lineno">  282</span>&#160;<span class="comment">// 2-dimensional coordinates to the output tensor dimensions.</span></div>
<div class="line"><a name="l00283"></a><span class="lineno">  283</span>&#160;<span class="keyword">struct </span>TensorContractionParams {</div>
<div class="line"><a name="l00284"></a><span class="lineno">  284</span>&#160;  <span class="comment">// TensorContraction evaluator assumes that both tensors are in ColMajor</span></div>
<div class="line"><a name="l00285"></a><span class="lineno">  285</span>&#160;  <span class="comment">// layout, if tensors are in RowMajor evaluator swap lhs with rhs.</span></div>
<div class="line"><a name="l00286"></a><span class="lineno">  286</span>&#160;  <span class="keywordtype">bool</span> swapped_arguments;</div>
<div class="line"><a name="l00287"></a><span class="lineno">  287</span>&#160;};</div>
<div class="line"><a name="l00288"></a><span class="lineno">  288</span>&#160; </div>
<div class="line"><a name="l00289"></a><span class="lineno">  289</span>&#160;<span class="comment">// Output kernel allows to fuse operations into the tensor contraction.</span></div>
<div class="line"><a name="l00290"></a><span class="lineno">  290</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00291"></a><span class="lineno">  291</span>&#160;<span class="comment">// Examples:</span></div>
<div class="line"><a name="l00292"></a><span class="lineno">  292</span>&#160;<span class="comment">//   1. Elementwise Relu transformation following Conv2D.</span></div>
<div class="line"><a name="l00293"></a><span class="lineno">  293</span>&#160;<span class="comment">//   2. AddBias to the Conv2D output channels dimension.</span></div>
<div class="line"><a name="l00294"></a><span class="lineno">  294</span>&#160;<span class="comment">//</span></div>
<div class="line"><a name="l00295"></a><span class="lineno">  295</span>&#160;<span class="comment">// The NoOpOutputKernel implements an output kernel that does absolutely nothing.</span></div>
<div class="line"><a name="l00296"></a><span class="lineno">  296</span>&#160;<span class="keyword">struct </span>NoOpOutputKernel {</div>
<div class="line"><a name="l00312"></a><span class="lineno">  312</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> Index, <span class="keyword">typename</span> Scalar&gt;</div>
<div class="line"><a name="l00313"></a><span class="lineno">  313</span>&#160;  EIGEN_ALWAYS_INLINE <span class="keywordtype">void</span> operator()(</div>
<div class="line"><a name="l00314"></a><span class="lineno">  314</span>&#160;      <span class="keyword">const</span> internal::blas_data_mapper&lt;Scalar, Index, ColMajor&gt;&amp; output_mapper,</div>
<div class="line"><a name="l00315"></a><span class="lineno">  315</span>&#160;      <span class="keyword">const</span> TensorContractionParams&amp; params, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i,</div>
<div class="line"><a name="l00316"></a><span class="lineno">  316</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> j, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> num_rows, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> num_cols)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00317"></a><span class="lineno">  317</span>&#160;    EIGEN_UNUSED_VARIABLE(output_mapper);</div>
<div class="line"><a name="l00318"></a><span class="lineno">  318</span>&#160;    EIGEN_UNUSED_VARIABLE(params);</div>
<div class="line"><a name="l00319"></a><span class="lineno">  319</span>&#160;    EIGEN_UNUSED_VARIABLE(i);</div>
<div class="line"><a name="l00320"></a><span class="lineno">  320</span>&#160;    EIGEN_UNUSED_VARIABLE(j);</div>
<div class="line"><a name="l00321"></a><span class="lineno">  321</span>&#160;    EIGEN_UNUSED_VARIABLE(num_rows);</div>
<div class="line"><a name="l00322"></a><span class="lineno">  322</span>&#160;    EIGEN_UNUSED_VARIABLE(num_cols);</div>
<div class="line"><a name="l00323"></a><span class="lineno">  323</span>&#160;  }</div>
<div class="line"><a name="l00324"></a><span class="lineno">  324</span>&#160;};</div>
<div class="line"><a name="l00325"></a><span class="lineno">  325</span>&#160; </div>
<div class="line"><a name="l00326"></a><span class="lineno">  326</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Indices, <span class="keyword">typename</span> LhsXprType, <span class="keyword">typename</span> RhsXprType, <span class="keyword">typename</span> OutputKernelType = const NoOpOutputKernel&gt;</div>
<div class="line"><a name="l00327"></a><span class="lineno">  327</span>&#160;<span class="keyword">class </span>TensorContractionOp : <span class="keyword">public</span> TensorBase&lt;TensorContractionOp&lt;Indices, LhsXprType, RhsXprType, OutputKernelType&gt;, ReadOnlyAccessors&gt;</div>
<div class="line"><a name="l00328"></a><span class="lineno">  328</span>&#160;{</div>
<div class="line"><a name="l00329"></a><span class="lineno">  329</span>&#160;  <span class="keyword">public</span>:</div>
<div class="line"><a name="l00330"></a><span class="lineno">  330</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen::internal::traits&lt;TensorContractionOp&gt;::Scalar Scalar;</div>
<div class="line"><a name="l00331"></a><span class="lineno">  331</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::gebp_traits&lt;<span class="keyword">typename</span> LhsXprType::CoeffReturnType,</div>
<div class="line"><a name="l00332"></a><span class="lineno">  332</span>&#160;                                         <span class="keyword">typename</span> RhsXprType::CoeffReturnType&gt;::ResScalar CoeffReturnType;</div>
<div class="line"><a name="l00333"></a><span class="lineno">  333</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen::internal::nested&lt;TensorContractionOp&gt;::type Nested;</div>
<div class="line"><a name="l00334"></a><span class="lineno">  334</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen::internal::traits&lt;TensorContractionOp&gt;::StorageKind StorageKind;</div>
<div class="line"><a name="l00335"></a><span class="lineno">  335</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Eigen::internal::traits&lt;TensorContractionOp&gt;::Index <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00336"></a><span class="lineno">  336</span>&#160; </div>
<div class="line"><a name="l00337"></a><span class="lineno">  337</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionOp(</div>
<div class="line"><a name="l00338"></a><span class="lineno">  338</span>&#160;      <span class="keyword">const</span> LhsXprType&amp; lhs, <span class="keyword">const</span> RhsXprType&amp; rhs, <span class="keyword">const</span> Indices&amp; dims,</div>
<div class="line"><a name="l00339"></a><span class="lineno">  339</span>&#160;      <span class="keyword">const</span> OutputKernelType&amp; output_kernel = OutputKernelType())</div>
<div class="line"><a name="l00340"></a><span class="lineno">  340</span>&#160;      : m_lhs_xpr(lhs), m_rhs_xpr(rhs), m_indices(dims),</div>
<div class="line"><a name="l00341"></a><span class="lineno">  341</span>&#160;        m_output_kernel(output_kernel) {}</div>
<div class="line"><a name="l00342"></a><span class="lineno">  342</span>&#160; </div>
<div class="line"><a name="l00343"></a><span class="lineno">  343</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00344"></a><span class="lineno">  344</span>&#160;  <span class="keyword">const</span> Indices&amp; indices()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_indices; }</div>
<div class="line"><a name="l00345"></a><span class="lineno">  345</span>&#160; </div>
<div class="line"><a name="l00347"></a><span class="lineno">  347</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00348"></a><span class="lineno">  348</span>&#160;  <span class="keyword">const</span> internal::remove_all_t&lt;typename LhsXprType::Nested&gt;&amp;</div>
<div class="line"><a name="l00349"></a><span class="lineno">  349</span>&#160;  lhsExpression()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_lhs_xpr; }</div>
<div class="line"><a name="l00350"></a><span class="lineno">  350</span>&#160; </div>
<div class="line"><a name="l00351"></a><span class="lineno">  351</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00352"></a><span class="lineno">  352</span>&#160;  <span class="keyword">const</span> internal::remove_all_t&lt;typename RhsXprType::Nested&gt;&amp;</div>
<div class="line"><a name="l00353"></a><span class="lineno">  353</span>&#160;  rhsExpression()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_rhs_xpr; }</div>
<div class="line"><a name="l00354"></a><span class="lineno">  354</span>&#160; </div>
<div class="line"><a name="l00355"></a><span class="lineno">  355</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00356"></a><span class="lineno">  356</span>&#160;  <span class="keyword">const</span> OutputKernelType&amp; outputKernel()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_output_kernel; }</div>
<div class="line"><a name="l00357"></a><span class="lineno">  357</span>&#160; </div>
<div class="line"><a name="l00358"></a><span class="lineno">  358</span>&#160;  <span class="keyword">protected</span>:</div>
<div class="line"><a name="l00359"></a><span class="lineno">  359</span>&#160;    <span class="keyword">typename</span> LhsXprType::Nested m_lhs_xpr;</div>
<div class="line"><a name="l00360"></a><span class="lineno">  360</span>&#160;    <span class="keyword">typename</span> RhsXprType::Nested m_rhs_xpr;</div>
<div class="line"><a name="l00361"></a><span class="lineno">  361</span>&#160;    <span class="keyword">const</span> Indices m_indices;</div>
<div class="line"><a name="l00362"></a><span class="lineno">  362</span>&#160;    <span class="keyword">const</span> OutputKernelType m_output_kernel;</div>
<div class="line"><a name="l00363"></a><span class="lineno">  363</span>&#160;};</div>
<div class="line"><a name="l00364"></a><span class="lineno">  364</span>&#160; </div>
<div class="line"><a name="l00365"></a><span class="lineno">  365</span>&#160; </div>
<div class="line"><a name="l00366"></a><span class="lineno">  366</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Derived&gt;</div>
<div class="line"><a name="l00367"></a><span class="lineno">  367</span>&#160;<span class="keyword">struct </span>TensorContractionEvaluatorBase : internal::no_assignment_operator</div>
<div class="line"><a name="l00368"></a><span class="lineno">  368</span>&#160;{</div>
<div class="line"><a name="l00369"></a><span class="lineno">  369</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::traits&lt;Derived&gt;::Indices Indices;</div>
<div class="line"><a name="l00370"></a><span class="lineno">  370</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::traits&lt;Derived&gt;::LeftArgType LeftArgType;</div>
<div class="line"><a name="l00371"></a><span class="lineno">  371</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::traits&lt;Derived&gt;::RightArgType RightArgType;</div>
<div class="line"><a name="l00372"></a><span class="lineno">  372</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::traits&lt;Derived&gt;::OutputKernelType OutputKernelType;</div>
<div class="line"><a name="l00373"></a><span class="lineno">  373</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> internal::traits&lt;Derived&gt;::Device Device;</div>
<div class="line"><a name="l00374"></a><span class="lineno">  374</span>&#160; </div>
<div class="line"><a name="l00375"></a><span class="lineno">  375</span>&#160;  <span class="keyword">typedef</span> TensorContractionOp&lt;Indices, LeftArgType, RightArgType, OutputKernelType&gt; XprType;</div>
<div class="line"><a name="l00376"></a><span class="lineno">  376</span>&#160;  <span class="keyword">typedef</span> std::remove_const_t&lt;typename XprType::Scalar&gt; Scalar;</div>
<div class="line"><a name="l00377"></a><span class="lineno">  377</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::Index <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00378"></a><span class="lineno">  378</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::CoeffReturnType CoeffReturnType;</div>
<div class="line"><a name="l00379"></a><span class="lineno">  379</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> PacketType&lt;CoeffReturnType, Device&gt;::type PacketReturnType;</div>
<div class="line"><a name="l00380"></a><span class="lineno">  380</span>&#160;  <span class="keyword">typedef</span> StorageMemory&lt;Scalar, Device&gt; Storage;</div>
<div class="line"><a name="l00381"></a><span class="lineno">  381</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> Storage::Type EvaluatorPointerType;</div>
<div class="line"><a name="l00382"></a><span class="lineno">  382</span>&#160; </div>
<div class="line"><a name="l00383"></a><span class="lineno">  383</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> Layout = TensorEvaluator&lt;LeftArgType, Device&gt;::Layout;</div>
<div class="line"><a name="l00384"></a><span class="lineno">  384</span>&#160;  <span class="keyword">enum</span> {</div>
<div class="line"><a name="l00385"></a><span class="lineno">  385</span>&#160;    IsAligned         = <span class="keyword">true</span>,</div>
<div class="line"><a name="l00386"></a><span class="lineno">  386</span>&#160;    PacketAccess      = (PacketType&lt;CoeffReturnType, Device&gt;::size &gt; 1),</div>
<div class="line"><a name="l00387"></a><span class="lineno">  387</span>&#160;    BlockAccess       = <span class="keyword">false</span>,</div>
<div class="line"><a name="l00388"></a><span class="lineno">  388</span>&#160;    PreferBlockAccess = <span class="keyword">false</span>,</div>
<div class="line"><a name="l00389"></a><span class="lineno">  389</span>&#160;    CoordAccess       = <span class="keyword">false</span>,  <span class="comment">// to be implemented</span></div>
<div class="line"><a name="l00390"></a><span class="lineno">  390</span>&#160;    RawAccess         = <span class="keyword">true</span></div>
<div class="line"><a name="l00391"></a><span class="lineno">  391</span>&#160;  };</div>
<div class="line"><a name="l00392"></a><span class="lineno">  392</span>&#160; </div>
<div class="line"><a name="l00393"></a><span class="lineno">  393</span>&#160;  <span class="comment">//===- Tensor block evaluation strategy (see TensorBlock.h) -------------===//</span></div>
<div class="line"><a name="l00394"></a><span class="lineno">  394</span>&#160;  <span class="keyword">typedef</span> internal::TensorBlockNotImplemented TensorBlock;</div>
<div class="line"><a name="l00395"></a><span class="lineno">  395</span>&#160;  <span class="comment">//===--------------------------------------------------------------------===//</span></div>
<div class="line"><a name="l00396"></a><span class="lineno">  396</span>&#160; </div>
<div class="line"><a name="l00397"></a><span class="lineno">  397</span>&#160;  <span class="comment">// Most of the code is assuming that both input tensors are ColMajor. If the</span></div>
<div class="line"><a name="l00398"></a><span class="lineno">  398</span>&#160;  <span class="comment">// inputs are RowMajor, we will &quot;cheat&quot; by swapping the LHS and RHS:</span></div>
<div class="line"><a name="l00399"></a><span class="lineno">  399</span>&#160;  <span class="comment">// If we want to compute A * B = C, where A is LHS and B is RHS, the code</span></div>
<div class="line"><a name="l00400"></a><span class="lineno">  400</span>&#160;  <span class="comment">// will pretend B is LHS and A is RHS.</span></div>
<div class="line"><a name="l00401"></a><span class="lineno">  401</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;</div>
<div class="line"><a name="l00402"></a><span class="lineno">  402</span>&#160;    <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>), LeftArgType, RightArgType&gt; EvalLeftArgType;</div>
<div class="line"><a name="l00403"></a><span class="lineno">  403</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;</div>
<div class="line"><a name="l00404"></a><span class="lineno">  404</span>&#160;    <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>), RightArgType, LeftArgType&gt; EvalRightArgType;</div>
<div class="line"><a name="l00405"></a><span class="lineno">  405</span>&#160; </div>
<div class="line"><a name="l00406"></a><span class="lineno">  406</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;EvalLeftArgType, Device&gt; LeftEvaluatorType;</div>
<div class="line"><a name="l00407"></a><span class="lineno">  407</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;EvalRightArgType, Device&gt; RightEvaluatorType;</div>
<div class="line"><a name="l00408"></a><span class="lineno">  408</span>&#160; </div>
<div class="line"><a name="l00409"></a><span class="lineno">  409</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> LDims =</div>
<div class="line"><a name="l00410"></a><span class="lineno">  410</span>&#160;      internal::array_size&lt;typename TensorEvaluator&lt;EvalLeftArgType, Device&gt;::Dimensions&gt;::value;</div>
<div class="line"><a name="l00411"></a><span class="lineno">  411</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> RDims =</div>
<div class="line"><a name="l00412"></a><span class="lineno">  412</span>&#160;      internal::array_size&lt;typename TensorEvaluator&lt;EvalRightArgType, Device&gt;::Dimensions&gt;::value;</div>
<div class="line"><a name="l00413"></a><span class="lineno">  413</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> ContractDims = internal::array_size&lt;Indices&gt;::value;</div>
<div class="line"><a name="l00414"></a><span class="lineno">  414</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDims = LDims + RDims - 2 * ContractDims;</div>
<div class="line"><a name="l00415"></a><span class="lineno">  415</span>&#160; </div>
<div class="line"><a name="l00416"></a><span class="lineno">  416</span>&#160;  <span class="keyword">typedef</span> array&lt;Index, ContractDims&gt; contract_t;</div>
<div class="line"><a name="l00417"></a><span class="lineno">  417</span>&#160;  <span class="keyword">typedef</span> array&lt;<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, LDims - ContractDims&gt; left_nocontract_t;</div>
<div class="line"><a name="l00418"></a><span class="lineno">  418</span>&#160;  <span class="keyword">typedef</span> array&lt;<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, RDims - ContractDims&gt; right_nocontract_t;</div>
<div class="line"><a name="l00419"></a><span class="lineno">  419</span>&#160; </div>
<div class="line"><a name="l00420"></a><span class="lineno">  420</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;Index, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l00421"></a><span class="lineno">  421</span>&#160; </div>
<div class="line"><a name="l00422"></a><span class="lineno">  422</span>&#160;  EIGEN_STRONG_INLINE</div>
<div class="line"><a name="l00423"></a><span class="lineno">  423</span>&#160;  TensorContractionEvaluatorBase(<span class="keyword">const</span> XprType&amp; op, <span class="keyword">const</span> Device&amp; device)</div>
<div class="line"><a name="l00424"></a><span class="lineno">  424</span>&#160;      : m_leftImpl(choose(Cond&lt;static_cast&lt;int&gt;(Layout) == static_cast&lt;int&gt;(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>)&gt;(),</div>
<div class="line"><a name="l00425"></a><span class="lineno">  425</span>&#160;                          op.lhsExpression(), op.rhsExpression()), device),</div>
<div class="line"><a name="l00426"></a><span class="lineno">  426</span>&#160;        m_rightImpl(choose(Cond&lt;static_cast&lt;int&gt;(Layout) == static_cast&lt;int&gt;(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>)&gt;(),</div>
<div class="line"><a name="l00427"></a><span class="lineno">  427</span>&#160;                           op.rhsExpression(), op.lhsExpression()), device),</div>
<div class="line"><a name="l00428"></a><span class="lineno">  428</span>&#160;        m_device(device),</div>
<div class="line"><a name="l00429"></a><span class="lineno">  429</span>&#160;        m_output_kernel(op.outputKernel()),</div>
<div class="line"><a name="l00430"></a><span class="lineno">  430</span>&#160;        m_result(NULL) {</div>
<div class="line"><a name="l00431"></a><span class="lineno">  431</span>&#160;    EIGEN_STATIC_ASSERT((<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(TensorEvaluator&lt;LeftArgType, Device&gt;::Layout) ==</div>
<div class="line"><a name="l00432"></a><span class="lineno">  432</span>&#160;         <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(TensorEvaluator&lt;RightArgType, Device&gt;::Layout)),</div>
<div class="line"><a name="l00433"></a><span class="lineno">  433</span>&#160;                        YOU_MADE_A_PROGRAMMING_MISTAKE);</div>
<div class="line"><a name="l00434"></a><span class="lineno">  434</span>&#160; </div>
<div class="line"><a name="l00435"></a><span class="lineno">  435</span>&#160; </div>
<div class="line"><a name="l00436"></a><span class="lineno">  436</span>&#160;    DSizes&lt;Index, LDims&gt; eval_left_dims;</div>
<div class="line"><a name="l00437"></a><span class="lineno">  437</span>&#160;    DSizes&lt;Index, RDims&gt; eval_right_dims;</div>
<div class="line"><a name="l00438"></a><span class="lineno">  438</span>&#160;    array&lt;IndexPair&lt;Index&gt;, ContractDims&gt; eval_op_indices;</div>
<div class="line"><a name="l00439"></a><span class="lineno">  439</span>&#160;    <span class="keywordflow">if</span> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>)) {</div>
<div class="line"><a name="l00440"></a><span class="lineno">  440</span>&#160;      <span class="comment">// For ColMajor, we keep using the existing dimensions</span></div>
<div class="line"><a name="l00441"></a><span class="lineno">  441</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; LDims; i++) {</div>
<div class="line"><a name="l00442"></a><span class="lineno">  442</span>&#160;        eval_left_dims[i] = m_leftImpl.dimensions()[i];</div>
<div class="line"><a name="l00443"></a><span class="lineno">  443</span>&#160;      }</div>
<div class="line"><a name="l00444"></a><span class="lineno">  444</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; RDims; i++) {</div>
<div class="line"><a name="l00445"></a><span class="lineno">  445</span>&#160;        eval_right_dims[i] = m_rightImpl.dimensions()[i];</div>
<div class="line"><a name="l00446"></a><span class="lineno">  446</span>&#160;      }</div>
<div class="line"><a name="l00447"></a><span class="lineno">  447</span>&#160;      <span class="comment">// We keep the pairs of contracting indices.</span></div>
<div class="line"><a name="l00448"></a><span class="lineno">  448</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; ContractDims; i++) {</div>
<div class="line"><a name="l00449"></a><span class="lineno">  449</span>&#160;        eval_op_indices[i].first = op.indices()[i].first;</div>
<div class="line"><a name="l00450"></a><span class="lineno">  450</span>&#160;        eval_op_indices[i].second = op.indices()[i].second;</div>
<div class="line"><a name="l00451"></a><span class="lineno">  451</span>&#160;      }</div>
<div class="line"><a name="l00452"></a><span class="lineno">  452</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00453"></a><span class="lineno">  453</span>&#160;      <span class="comment">// For RowMajor, we need to reverse the existing dimensions</span></div>
<div class="line"><a name="l00454"></a><span class="lineno">  454</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; LDims; i++) {</div>
<div class="line"><a name="l00455"></a><span class="lineno">  455</span>&#160;        eval_left_dims[i] = m_leftImpl.dimensions()[LDims - i - 1];</div>
<div class="line"><a name="l00456"></a><span class="lineno">  456</span>&#160;      }</div>
<div class="line"><a name="l00457"></a><span class="lineno">  457</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; RDims; i++) {</div>
<div class="line"><a name="l00458"></a><span class="lineno">  458</span>&#160;        eval_right_dims[i] = m_rightImpl.dimensions()[RDims - i - 1];</div>
<div class="line"><a name="l00459"></a><span class="lineno">  459</span>&#160;      }</div>
<div class="line"><a name="l00460"></a><span class="lineno">  460</span>&#160;      <span class="comment">// We need to flip all the pairs of contracting indices as well as</span></div>
<div class="line"><a name="l00461"></a><span class="lineno">  461</span>&#160;      <span class="comment">// reversing the dimensions.</span></div>
<div class="line"><a name="l00462"></a><span class="lineno">  462</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; ContractDims; i++) {</div>
<div class="line"><a name="l00463"></a><span class="lineno">  463</span>&#160;        eval_op_indices[i].first = LDims - 1 - op.indices()[ContractDims - 1 - i].second;</div>
<div class="line"><a name="l00464"></a><span class="lineno">  464</span>&#160;        eval_op_indices[i].second = RDims - 1 - op.indices()[ContractDims - 1 - i].first;</div>
<div class="line"><a name="l00465"></a><span class="lineno">  465</span>&#160;      }</div>
<div class="line"><a name="l00466"></a><span class="lineno">  466</span>&#160;    }</div>
<div class="line"><a name="l00467"></a><span class="lineno">  467</span>&#160; </div>
<div class="line"><a name="l00468"></a><span class="lineno">  468</span>&#160;    <span class="comment">// Check for duplicate axes and make sure the first index in eval_op_indices</span></div>
<div class="line"><a name="l00469"></a><span class="lineno">  469</span>&#160;    <span class="comment">// is increasing. Using O(n^2) sorting is OK since ContractDims is small</span></div>
<div class="line"><a name="l00470"></a><span class="lineno">  470</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; ContractDims; i++) {</div>
<div class="line"><a name="l00471"></a><span class="lineno">  471</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = i + 1; j &lt; ContractDims; j++) {</div>
<div class="line"><a name="l00472"></a><span class="lineno">  472</span>&#160;        eigen_assert(eval_op_indices[j].first != eval_op_indices[i].first &amp;&amp;</div>
<div class="line"><a name="l00473"></a><span class="lineno">  473</span>&#160;                     eval_op_indices[j].second != eval_op_indices[i].second &amp;&amp;</div>
<div class="line"><a name="l00474"></a><span class="lineno">  474</span>&#160;                     <span class="stringliteral">&quot;contraction axes should be unique&quot;</span>);</div>
<div class="line"><a name="l00475"></a><span class="lineno">  475</span>&#160;        <span class="keywordflow">if</span> (eval_op_indices[j].first &lt; eval_op_indices[i].first) {</div>
<div class="line"><a name="l00476"></a><span class="lineno">  476</span>&#160;          numext::swap(eval_op_indices[j], eval_op_indices[i]);</div>
<div class="line"><a name="l00477"></a><span class="lineno">  477</span>&#160;        }</div>
<div class="line"><a name="l00478"></a><span class="lineno">  478</span>&#160;      }</div>
<div class="line"><a name="l00479"></a><span class="lineno">  479</span>&#160;    }</div>
<div class="line"><a name="l00480"></a><span class="lineno">  480</span>&#160; </div>
<div class="line"><a name="l00481"></a><span class="lineno">  481</span>&#160;    array&lt;Index, LDims&gt; lhs_strides;</div>
<div class="line"><a name="l00482"></a><span class="lineno">  482</span>&#160;    lhs_strides[0] = 1;</div>
<div class="line"><a name="l00483"></a><span class="lineno">  483</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; LDims-1; ++i) {</div>
<div class="line"><a name="l00484"></a><span class="lineno">  484</span>&#160;      lhs_strides[i+1] = lhs_strides[i] * eval_left_dims[i];</div>
<div class="line"><a name="l00485"></a><span class="lineno">  485</span>&#160;    }</div>
<div class="line"><a name="l00486"></a><span class="lineno">  486</span>&#160; </div>
<div class="line"><a name="l00487"></a><span class="lineno">  487</span>&#160;    array&lt;Index, RDims&gt; rhs_strides;</div>
<div class="line"><a name="l00488"></a><span class="lineno">  488</span>&#160;    rhs_strides[0] = 1;</div>
<div class="line"><a name="l00489"></a><span class="lineno">  489</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; RDims-1; ++i) {</div>
<div class="line"><a name="l00490"></a><span class="lineno">  490</span>&#160;      rhs_strides[i+1] = rhs_strides[i] * eval_right_dims[i];</div>
<div class="line"><a name="l00491"></a><span class="lineno">  491</span>&#160;    }</div>
<div class="line"><a name="l00492"></a><span class="lineno">  492</span>&#160; </div>
<div class="line"><a name="l00493"></a><span class="lineno">  493</span>&#160;    <span class="keywordflow">if</span> (m_i_strides.size() &gt; 0) m_i_strides[0] = 1;</div>
<div class="line"><a name="l00494"></a><span class="lineno">  494</span>&#160;    <span class="keywordflow">if</span> (m_j_strides.size() &gt; 0) m_j_strides[0] = 1;</div>
<div class="line"><a name="l00495"></a><span class="lineno">  495</span>&#160;    <span class="keywordflow">if</span> (m_k_strides.size() &gt; 0) m_k_strides[0] = 1;</div>
<div class="line"><a name="l00496"></a><span class="lineno">  496</span>&#160; </div>
<div class="line"><a name="l00497"></a><span class="lineno">  497</span>&#160;    m_i_size = 1;</div>
<div class="line"><a name="l00498"></a><span class="lineno">  498</span>&#160;    m_j_size = 1;</div>
<div class="line"><a name="l00499"></a><span class="lineno">  499</span>&#160;    m_k_size = 1;</div>
<div class="line"><a name="l00500"></a><span class="lineno">  500</span>&#160; </div>
<div class="line"><a name="l00501"></a><span class="lineno">  501</span>&#160;    <span class="comment">// To compute the dimension, we simply concatenate the non-contracting</span></div>
<div class="line"><a name="l00502"></a><span class="lineno">  502</span>&#160;    <span class="comment">// dimensions of the left and then the right tensor. Additionally, we also</span></div>
<div class="line"><a name="l00503"></a><span class="lineno">  503</span>&#160;    <span class="comment">// compute the strides corresponding to the left non-contracting</span></div>
<div class="line"><a name="l00504"></a><span class="lineno">  504</span>&#160;    <span class="comment">// dimensions and right non-contracting dimensions.</span></div>
<div class="line"><a name="l00505"></a><span class="lineno">  505</span>&#160;    m_lhs_inner_dim_contiguous = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00506"></a><span class="lineno">  506</span>&#160;    <span class="keywordtype">int</span> dim_idx = 0;</div>
<div class="line"><a name="l00507"></a><span class="lineno">  507</span>&#160;    <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> nocontract_idx = 0;</div>
<div class="line"><a name="l00508"></a><span class="lineno">  508</span>&#160; </div>
<div class="line"><a name="l00509"></a><span class="lineno">  509</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; LDims; i++) {</div>
<div class="line"><a name="l00510"></a><span class="lineno">  510</span>&#160;      <span class="comment">// find if we are contracting on index i of left tensor</span></div>
<div class="line"><a name="l00511"></a><span class="lineno">  511</span>&#160;      <span class="keywordtype">bool</span> contracting = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00512"></a><span class="lineno">  512</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; ContractDims; j++) {</div>
<div class="line"><a name="l00513"></a><span class="lineno">  513</span>&#160;        <span class="keywordflow">if</span> (eval_op_indices[j].first == i) {</div>
<div class="line"><a name="l00514"></a><span class="lineno">  514</span>&#160;          contracting = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00515"></a><span class="lineno">  515</span>&#160;          <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00516"></a><span class="lineno">  516</span>&#160;        }</div>
<div class="line"><a name="l00517"></a><span class="lineno">  517</span>&#160;      }</div>
<div class="line"><a name="l00518"></a><span class="lineno">  518</span>&#160;      <span class="keywordflow">if</span> (!contracting) {</div>
<div class="line"><a name="l00519"></a><span class="lineno">  519</span>&#160;        <span class="comment">// add dimension size to output dimensions</span></div>
<div class="line"><a name="l00520"></a><span class="lineno">  520</span>&#160;        m_dimensions[dim_idx] = eval_left_dims[i];</div>
<div class="line"><a name="l00521"></a><span class="lineno">  521</span>&#160;        m_left_nocontract_strides[nocontract_idx] = lhs_strides[i];</div>
<div class="line"><a name="l00522"></a><span class="lineno">  522</span>&#160;        <span class="keywordflow">if</span> (dim_idx != i) {</div>
<div class="line"><a name="l00523"></a><span class="lineno">  523</span>&#160;          m_lhs_inner_dim_contiguous = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00524"></a><span class="lineno">  524</span>&#160;        }</div>
<div class="line"><a name="l00525"></a><span class="lineno">  525</span>&#160;        <span class="keywordflow">if</span> (nocontract_idx+1 &lt; internal::array_size&lt;left_nocontract_t&gt;::value) {</div>
<div class="line"><a name="l00526"></a><span class="lineno">  526</span>&#160;          m_i_strides[nocontract_idx+1] =</div>
<div class="line"><a name="l00527"></a><span class="lineno">  527</span>&#160;              m_i_strides[nocontract_idx] * eval_left_dims[i];</div>
<div class="line"><a name="l00528"></a><span class="lineno">  528</span>&#160;        } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00529"></a><span class="lineno">  529</span>&#160;          m_i_size = m_i_strides[nocontract_idx] * eval_left_dims[i];</div>
<div class="line"><a name="l00530"></a><span class="lineno">  530</span>&#160;        }</div>
<div class="line"><a name="l00531"></a><span class="lineno">  531</span>&#160;        dim_idx++;</div>
<div class="line"><a name="l00532"></a><span class="lineno">  532</span>&#160;        nocontract_idx++;</div>
<div class="line"><a name="l00533"></a><span class="lineno">  533</span>&#160;      }</div>
<div class="line"><a name="l00534"></a><span class="lineno">  534</span>&#160;    }</div>
<div class="line"><a name="l00535"></a><span class="lineno">  535</span>&#160; </div>
<div class="line"><a name="l00536"></a><span class="lineno">  536</span>&#160;    nocontract_idx = 0;</div>
<div class="line"><a name="l00537"></a><span class="lineno">  537</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; RDims; i++) {</div>
<div class="line"><a name="l00538"></a><span class="lineno">  538</span>&#160;      <span class="keywordtype">bool</span> contracting = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00539"></a><span class="lineno">  539</span>&#160;      <span class="comment">// find if we are contracting on index i of right tensor</span></div>
<div class="line"><a name="l00540"></a><span class="lineno">  540</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> j = 0; j &lt; ContractDims; j++) {</div>
<div class="line"><a name="l00541"></a><span class="lineno">  541</span>&#160;        <span class="keywordflow">if</span> (eval_op_indices[j].second == i) {</div>
<div class="line"><a name="l00542"></a><span class="lineno">  542</span>&#160;          contracting = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00543"></a><span class="lineno">  543</span>&#160;          <span class="keywordflow">break</span>;</div>
<div class="line"><a name="l00544"></a><span class="lineno">  544</span>&#160;        }</div>
<div class="line"><a name="l00545"></a><span class="lineno">  545</span>&#160;      }</div>
<div class="line"><a name="l00546"></a><span class="lineno">  546</span>&#160;      <span class="keywordflow">if</span> (!contracting) {</div>
<div class="line"><a name="l00547"></a><span class="lineno">  547</span>&#160;        m_dimensions[dim_idx] = eval_right_dims[i];</div>
<div class="line"><a name="l00548"></a><span class="lineno">  548</span>&#160;        <span class="keywordflow">if</span> (nocontract_idx+1 &lt; internal::array_size&lt;right_nocontract_t&gt;::value) {</div>
<div class="line"><a name="l00549"></a><span class="lineno">  549</span>&#160;          m_j_strides[nocontract_idx+1] =</div>
<div class="line"><a name="l00550"></a><span class="lineno">  550</span>&#160;              m_j_strides[nocontract_idx] * eval_right_dims[i];</div>
<div class="line"><a name="l00551"></a><span class="lineno">  551</span>&#160;        } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00552"></a><span class="lineno">  552</span>&#160;          m_j_size = m_j_strides[nocontract_idx] * eval_right_dims[i];</div>
<div class="line"><a name="l00553"></a><span class="lineno">  553</span>&#160;        }</div>
<div class="line"><a name="l00554"></a><span class="lineno">  554</span>&#160;        m_right_nocontract_strides[nocontract_idx] = rhs_strides[i];</div>
<div class="line"><a name="l00555"></a><span class="lineno">  555</span>&#160;        dim_idx++;</div>
<div class="line"><a name="l00556"></a><span class="lineno">  556</span>&#160;        nocontract_idx++;</div>
<div class="line"><a name="l00557"></a><span class="lineno">  557</span>&#160;      }</div>
<div class="line"><a name="l00558"></a><span class="lineno">  558</span>&#160;    }</div>
<div class="line"><a name="l00559"></a><span class="lineno">  559</span>&#160; </div>
<div class="line"><a name="l00560"></a><span class="lineno">  560</span>&#160;    <span class="comment">// Now compute the strides corresponding to the contracting dimensions. We</span></div>
<div class="line"><a name="l00561"></a><span class="lineno">  561</span>&#160;    <span class="comment">// assumed above that non-contracting axes are represented in the same order</span></div>
<div class="line"><a name="l00562"></a><span class="lineno">  562</span>&#160;    <span class="comment">// in the matrix as they are in the tensor. This is not the case for</span></div>
<div class="line"><a name="l00563"></a><span class="lineno">  563</span>&#160;    <span class="comment">// contracting axes. As the contracting axes must be of the same size in</span></div>
<div class="line"><a name="l00564"></a><span class="lineno">  564</span>&#160;    <span class="comment">// each tensor, we&#39;ll only look at the first tensor here.</span></div>
<div class="line"><a name="l00565"></a><span class="lineno">  565</span>&#160;    m_rhs_inner_dim_contiguous = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00566"></a><span class="lineno">  566</span>&#160;    m_rhs_inner_dim_reordered = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00567"></a><span class="lineno">  567</span>&#160;    <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; ContractDims; i++) {</div>
<div class="line"><a name="l00568"></a><span class="lineno">  568</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> left = eval_op_indices[i].first;</div>
<div class="line"><a name="l00569"></a><span class="lineno">  569</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> right = eval_op_indices[i].second;</div>
<div class="line"><a name="l00570"></a><span class="lineno">  570</span>&#160; </div>
<div class="line"><a name="l00571"></a><span class="lineno">  571</span>&#160;      <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> size = eval_left_dims[left];</div>
<div class="line"><a name="l00572"></a><span class="lineno">  572</span>&#160;      eigen_assert(size == eval_right_dims[right] &amp;&amp;</div>
<div class="line"><a name="l00573"></a><span class="lineno">  573</span>&#160;                   <span class="stringliteral">&quot;Contraction axes must be same size&quot;</span>);</div>
<div class="line"><a name="l00574"></a><span class="lineno">  574</span>&#160; </div>
<div class="line"><a name="l00575"></a><span class="lineno">  575</span>&#160;      <span class="keywordflow">if</span> (i+1 &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(internal::array_size&lt;contract_t&gt;::value)) {</div>
<div class="line"><a name="l00576"></a><span class="lineno">  576</span>&#160;        m_k_strides[i+1] = m_k_strides[i] * size;</div>
<div class="line"><a name="l00577"></a><span class="lineno">  577</span>&#160;      } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00578"></a><span class="lineno">  578</span>&#160;        m_k_size = m_k_strides[i] * size;</div>
<div class="line"><a name="l00579"></a><span class="lineno">  579</span>&#160;      }</div>
<div class="line"><a name="l00580"></a><span class="lineno">  580</span>&#160;      m_left_contracting_strides[i] = lhs_strides[left];</div>
<div class="line"><a name="l00581"></a><span class="lineno">  581</span>&#160;      m_right_contracting_strides[i] = rhs_strides[right];</div>
<div class="line"><a name="l00582"></a><span class="lineno">  582</span>&#160; </div>
<div class="line"><a name="l00583"></a><span class="lineno">  583</span>&#160;      <span class="keywordflow">if</span> (i &gt; 0 &amp;&amp; right &lt; eval_op_indices[i-1].second) {</div>
<div class="line"><a name="l00584"></a><span class="lineno">  584</span>&#160;        m_rhs_inner_dim_reordered = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00585"></a><span class="lineno">  585</span>&#160;      }</div>
<div class="line"><a name="l00586"></a><span class="lineno">  586</span>&#160;      <span class="keywordflow">if</span> (right != i) {</div>
<div class="line"><a name="l00587"></a><span class="lineno">  587</span>&#160;        m_rhs_inner_dim_contiguous = <span class="keyword">false</span>;</div>
<div class="line"><a name="l00588"></a><span class="lineno">  588</span>&#160;      }</div>
<div class="line"><a name="l00589"></a><span class="lineno">  589</span>&#160;    }</div>
<div class="line"><a name="l00590"></a><span class="lineno">  590</span>&#160; </div>
<div class="line"><a name="l00591"></a><span class="lineno">  591</span>&#160;    <span class="comment">// If the layout is RowMajor, we need to reverse the m_dimensions</span></div>
<div class="line"><a name="l00592"></a><span class="lineno">  592</span>&#160;    <span class="keywordflow">if</span> (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a77c993a8d9f6efe5c1159fb2ab07dd4f">RowMajor</a>)) {</div>
<div class="line"><a name="l00593"></a><span class="lineno">  593</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0, j = NumDims - 1; i &lt; j; i++, j--) {</div>
<div class="line"><a name="l00594"></a><span class="lineno">  594</span>&#160;        numext::swap(m_dimensions[i], m_dimensions[j]);</div>
<div class="line"><a name="l00595"></a><span class="lineno">  595</span>&#160;      }</div>
<div class="line"><a name="l00596"></a><span class="lineno">  596</span>&#160;    }</div>
<div class="line"><a name="l00597"></a><span class="lineno">  597</span>&#160; </div>
<div class="line"><a name="l00598"></a><span class="lineno">  598</span>&#160;    <span class="comment">// A set of parameters that will allow output kernel to get from output</span></div>
<div class="line"><a name="l00599"></a><span class="lineno">  599</span>&#160;    <span class="comment">// tensor dimensions (i, j) into the original tensor dimensions.</span></div>
<div class="line"><a name="l00600"></a><span class="lineno">  600</span>&#160;    <span class="comment">// TODO(ezhulenev): Add parameters required to infer output tensor index for</span></div>
<div class="line"><a name="l00601"></a><span class="lineno">  601</span>&#160;    <span class="comment">// more complex contractions than 2x2 on internal dimension.</span></div>
<div class="line"><a name="l00602"></a><span class="lineno">  602</span>&#160;    m_tensor_contraction_params.swapped_arguments = <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span>(Layout) == <a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a77c993a8d9f6efe5c1159fb2ab07dd4f">RowMajor</a>;</div>
<div class="line"><a name="l00603"></a><span class="lineno">  603</span>&#160;  }</div>
<div class="line"><a name="l00604"></a><span class="lineno">  604</span>&#160; </div>
<div class="line"><a name="l00605"></a><span class="lineno">  605</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE <span class="keyword">const</span> Dimensions&amp; dimensions()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_dimensions; }</div>
<div class="line"><a name="l00606"></a><span class="lineno">  606</span>&#160; </div>
<div class="line"><a name="l00607"></a><span class="lineno">  607</span>&#160;  EIGEN_STRONG_INLINE <span class="keywordtype">bool</span> evalSubExprsIfNeeded(EvaluatorPointerType data) {</div>
<div class="line"><a name="l00608"></a><span class="lineno">  608</span>&#160;    m_leftImpl.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00609"></a><span class="lineno">  609</span>&#160;    m_rightImpl.evalSubExprsIfNeeded(NULL);</div>
<div class="line"><a name="l00610"></a><span class="lineno">  610</span>&#160;    <span class="keywordflow">if</span> (data) {</div>
<div class="line"><a name="l00611"></a><span class="lineno">  611</span>&#160;      evalTo(data);</div>
<div class="line"><a name="l00612"></a><span class="lineno">  612</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">false</span>;</div>
<div class="line"><a name="l00613"></a><span class="lineno">  613</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00614"></a><span class="lineno">  614</span>&#160;      m_result = <span class="keyword">static_cast&lt;</span>EvaluatorPointerType<span class="keyword">&gt;</span>(m_device.allocate(dimensions().TotalSize() * <span class="keyword">sizeof</span>(Scalar)));</div>
<div class="line"><a name="l00615"></a><span class="lineno">  615</span>&#160;      evalTo(m_result);</div>
<div class="line"><a name="l00616"></a><span class="lineno">  616</span>&#160;      <span class="keywordflow">return</span> <span class="keyword">true</span>;</div>
<div class="line"><a name="l00617"></a><span class="lineno">  617</span>&#160;    }</div>
<div class="line"><a name="l00618"></a><span class="lineno">  618</span>&#160;  }</div>
<div class="line"><a name="l00619"></a><span class="lineno">  619</span>&#160; </div>
<div class="line"><a name="l00620"></a><span class="lineno">  620</span>&#160;<span class="preprocessor">#ifdef EIGEN_USE_THREADS</span></div>
<div class="line"><a name="l00621"></a><span class="lineno">  621</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> EvalSubExprsCallback&gt;</div>
<div class="line"><a name="l00622"></a><span class="lineno">  622</span>&#160;  EIGEN_STRONG_INLINE <span class="keywordtype">void</span> evalSubExprsIfNeededAsync(</div>
<div class="line"><a name="l00623"></a><span class="lineno">  623</span>&#160;      EvaluatorPointerType dest, EvalSubExprsCallback done) {</div>
<div class="line"><a name="l00624"></a><span class="lineno">  624</span>&#160;    m_leftImpl.evalSubExprsIfNeededAsync(<span class="keyword">nullptr</span>, [<span class="keyword">this</span>, done, dest](<span class="keywordtype">bool</span>) {</div>
<div class="line"><a name="l00625"></a><span class="lineno">  625</span>&#160;      m_rightImpl.evalSubExprsIfNeededAsync(<span class="keyword">nullptr</span>, [<span class="keyword">this</span>, done, dest](<span class="keywordtype">bool</span>) {</div>
<div class="line"><a name="l00626"></a><span class="lineno">  626</span>&#160;        <span class="keywordflow">if</span> (dest) {</div>
<div class="line"><a name="l00627"></a><span class="lineno">  627</span>&#160;          evalToAsync(dest, [done]() { done(<span class="keyword">false</span>); });</div>
<div class="line"><a name="l00628"></a><span class="lineno">  628</span>&#160;        } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00629"></a><span class="lineno">  629</span>&#160;          m_result = <span class="keyword">static_cast&lt;</span>EvaluatorPointerType<span class="keyword">&gt;</span>(</div>
<div class="line"><a name="l00630"></a><span class="lineno">  630</span>&#160;              m_device.allocate(dimensions().TotalSize() * <span class="keyword">sizeof</span>(Scalar)));</div>
<div class="line"><a name="l00631"></a><span class="lineno">  631</span>&#160;          evalToAsync(m_result, [done]() { done(<span class="keyword">true</span>); });</div>
<div class="line"><a name="l00632"></a><span class="lineno">  632</span>&#160;        }</div>
<div class="line"><a name="l00633"></a><span class="lineno">  633</span>&#160;      });</div>
<div class="line"><a name="l00634"></a><span class="lineno">  634</span>&#160;    });</div>
<div class="line"><a name="l00635"></a><span class="lineno">  635</span>&#160;  }</div>
<div class="line"><a name="l00636"></a><span class="lineno">  636</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_USE_THREADS</span></div>
<div class="line"><a name="l00637"></a><span class="lineno">  637</span>&#160; </div>
<div class="line"><a name="l00638"></a><span class="lineno">  638</span>&#160;<span class="preprocessor">#ifndef TENSOR_CONTRACTION_DISPATCH</span></div>
<div class="line"><a name="l00639"></a><span class="lineno">  639</span>&#160;<span class="preprocessor">#define TENSOR_CONTRACTION_DISPATCH(METHOD, ALIGNMENT, ARGS) \</span></div>
<div class="line"><a name="l00640"></a><span class="lineno">  640</span>&#160;<span class="preprocessor">  if (this-&gt;m_lhs_inner_dim_contiguous) {                    \</span></div>
<div class="line"><a name="l00641"></a><span class="lineno">  641</span>&#160;<span class="preprocessor">    if (this-&gt;m_rhs_inner_dim_contiguous) {                  \</span></div>
<div class="line"><a name="l00642"></a><span class="lineno">  642</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                 \</span></div>
<div class="line"><a name="l00643"></a><span class="lineno">  643</span>&#160;<span class="preprocessor">        METHOD&lt;true, true, true, ALIGNMENT&gt; ARGS;            \</span></div>
<div class="line"><a name="l00644"></a><span class="lineno">  644</span>&#160;<span class="preprocessor">      } else {                                               \</span></div>
<div class="line"><a name="l00645"></a><span class="lineno">  645</span>&#160;<span class="preprocessor">        METHOD&lt;true, true, false, ALIGNMENT&gt; ARGS;           \</span></div>
<div class="line"><a name="l00646"></a><span class="lineno">  646</span>&#160;<span class="preprocessor">      }                                                      \</span></div>
<div class="line"><a name="l00647"></a><span class="lineno">  647</span>&#160;<span class="preprocessor">    } else {                                                 \</span></div>
<div class="line"><a name="l00648"></a><span class="lineno">  648</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                 \</span></div>
<div class="line"><a name="l00649"></a><span class="lineno">  649</span>&#160;<span class="preprocessor">        METHOD&lt;true, false, true, ALIGNMENT&gt; ARGS;           \</span></div>
<div class="line"><a name="l00650"></a><span class="lineno">  650</span>&#160;<span class="preprocessor">      } else {                                               \</span></div>
<div class="line"><a name="l00651"></a><span class="lineno">  651</span>&#160;<span class="preprocessor">        METHOD&lt;true, false, false, ALIGNMENT&gt; ARGS;          \</span></div>
<div class="line"><a name="l00652"></a><span class="lineno">  652</span>&#160;<span class="preprocessor">      }                                                      \</span></div>
<div class="line"><a name="l00653"></a><span class="lineno">  653</span>&#160;<span class="preprocessor">    }                                                        \</span></div>
<div class="line"><a name="l00654"></a><span class="lineno">  654</span>&#160;<span class="preprocessor">  } else {                                                   \</span></div>
<div class="line"><a name="l00655"></a><span class="lineno">  655</span>&#160;<span class="preprocessor">    if (this-&gt;m_rhs_inner_dim_contiguous) {                  \</span></div>
<div class="line"><a name="l00656"></a><span class="lineno">  656</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                 \</span></div>
<div class="line"><a name="l00657"></a><span class="lineno">  657</span>&#160;<span class="preprocessor">        METHOD&lt;false, true, true, ALIGNMENT&gt; ARGS;           \</span></div>
<div class="line"><a name="l00658"></a><span class="lineno">  658</span>&#160;<span class="preprocessor">      } else {                                               \</span></div>
<div class="line"><a name="l00659"></a><span class="lineno">  659</span>&#160;<span class="preprocessor">        METHOD&lt;false, true, false, ALIGNMENT&gt; ARGS;          \</span></div>
<div class="line"><a name="l00660"></a><span class="lineno">  660</span>&#160;<span class="preprocessor">      }                                                      \</span></div>
<div class="line"><a name="l00661"></a><span class="lineno">  661</span>&#160;<span class="preprocessor">    } else {                                                 \</span></div>
<div class="line"><a name="l00662"></a><span class="lineno">  662</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                 \</span></div>
<div class="line"><a name="l00663"></a><span class="lineno">  663</span>&#160;<span class="preprocessor">        METHOD&lt;false, false, true, ALIGNMENT&gt; ARGS;          \</span></div>
<div class="line"><a name="l00664"></a><span class="lineno">  664</span>&#160;<span class="preprocessor">      } else {                                               \</span></div>
<div class="line"><a name="l00665"></a><span class="lineno">  665</span>&#160;<span class="preprocessor">        METHOD&lt;false, false, false, ALIGNMENT&gt; ARGS;         \</span></div>
<div class="line"><a name="l00666"></a><span class="lineno">  666</span>&#160;<span class="preprocessor">      }                                                      \</span></div>
<div class="line"><a name="l00667"></a><span class="lineno">  667</span>&#160;<span class="preprocessor">    }                                                        \</span></div>
<div class="line"><a name="l00668"></a><span class="lineno">  668</span>&#160;<span class="preprocessor">  }</span></div>
<div class="line"><a name="l00669"></a><span class="lineno">  669</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00670"></a><span class="lineno">  670</span>&#160; </div>
<div class="line"><a name="l00671"></a><span class="lineno">  671</span>&#160;<span class="preprocessor">#ifndef TENSOR_CONTRACTION_ASYNC_DISPATCH</span></div>
<div class="line"><a name="l00672"></a><span class="lineno">  672</span>&#160;<span class="preprocessor">#define TENSOR_CONTRACTION_ASYNC_DISPATCH(METHOD, DONE, ALIGNMENT, ARGS, FN) \</span></div>
<div class="line"><a name="l00673"></a><span class="lineno">  673</span>&#160;<span class="preprocessor">  if (this-&gt;m_lhs_inner_dim_contiguous) {                                    \</span></div>
<div class="line"><a name="l00674"></a><span class="lineno">  674</span>&#160;<span class="preprocessor">    if (this-&gt;m_rhs_inner_dim_contiguous) {                                  \</span></div>
<div class="line"><a name="l00675"></a><span class="lineno">  675</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                                 \</span></div>
<div class="line"><a name="l00676"></a><span class="lineno">  676</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, true, true, true, ALIGNMENT&gt; ARGS)-&gt;FN;            \</span></div>
<div class="line"><a name="l00677"></a><span class="lineno">  677</span>&#160;<span class="preprocessor">      } else {                                                               \</span></div>
<div class="line"><a name="l00678"></a><span class="lineno">  678</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, true, true, false, ALIGNMENT&gt; ARGS)-&gt;FN;           \</span></div>
<div class="line"><a name="l00679"></a><span class="lineno">  679</span>&#160;<span class="preprocessor">      }                                                                      \</span></div>
<div class="line"><a name="l00680"></a><span class="lineno">  680</span>&#160;<span class="preprocessor">    } else {                                                                 \</span></div>
<div class="line"><a name="l00681"></a><span class="lineno">  681</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                                 \</span></div>
<div class="line"><a name="l00682"></a><span class="lineno">  682</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, true, false, true, ALIGNMENT&gt; ARGS)-&gt;FN;           \</span></div>
<div class="line"><a name="l00683"></a><span class="lineno">  683</span>&#160;<span class="preprocessor">      } else {                                                               \</span></div>
<div class="line"><a name="l00684"></a><span class="lineno">  684</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, true, false, false, ALIGNMENT&gt; ARGS)-&gt;FN;          \</span></div>
<div class="line"><a name="l00685"></a><span class="lineno">  685</span>&#160;<span class="preprocessor">      }                                                                      \</span></div>
<div class="line"><a name="l00686"></a><span class="lineno">  686</span>&#160;<span class="preprocessor">    }                                                                        \</span></div>
<div class="line"><a name="l00687"></a><span class="lineno">  687</span>&#160;<span class="preprocessor">  } else {                                                                   \</span></div>
<div class="line"><a name="l00688"></a><span class="lineno">  688</span>&#160;<span class="preprocessor">    if (this-&gt;m_rhs_inner_dim_contiguous) {                                  \</span></div>
<div class="line"><a name="l00689"></a><span class="lineno">  689</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                                 \</span></div>
<div class="line"><a name="l00690"></a><span class="lineno">  690</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, false, true, true, ALIGNMENT&gt; ARGS)-&gt;FN;           \</span></div>
<div class="line"><a name="l00691"></a><span class="lineno">  691</span>&#160;<span class="preprocessor">      } else {                                                               \</span></div>
<div class="line"><a name="l00692"></a><span class="lineno">  692</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, false, true, false, ALIGNMENT&gt; ARGS)-&gt;FN;          \</span></div>
<div class="line"><a name="l00693"></a><span class="lineno">  693</span>&#160;<span class="preprocessor">      }                                                                      \</span></div>
<div class="line"><a name="l00694"></a><span class="lineno">  694</span>&#160;<span class="preprocessor">    } else {                                                                 \</span></div>
<div class="line"><a name="l00695"></a><span class="lineno">  695</span>&#160;<span class="preprocessor">      if (this-&gt;m_rhs_inner_dim_reordered) {                                 \</span></div>
<div class="line"><a name="l00696"></a><span class="lineno">  696</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, false, false, true, ALIGNMENT&gt; ARGS)-&gt;FN;          \</span></div>
<div class="line"><a name="l00697"></a><span class="lineno">  697</span>&#160;<span class="preprocessor">      } else {                                                               \</span></div>
<div class="line"><a name="l00698"></a><span class="lineno">  698</span>&#160;<span class="preprocessor">        (new METHOD&lt;DONE, false, false, false, ALIGNMENT&gt; ARGS)-&gt;FN;         \</span></div>
<div class="line"><a name="l00699"></a><span class="lineno">  699</span>&#160;<span class="preprocessor">      }                                                                      \</span></div>
<div class="line"><a name="l00700"></a><span class="lineno">  700</span>&#160;<span class="preprocessor">    }                                                                        \</span></div>
<div class="line"><a name="l00701"></a><span class="lineno">  701</span>&#160;<span class="preprocessor">  }</span></div>
<div class="line"><a name="l00702"></a><span class="lineno">  702</span>&#160;<span class="preprocessor">#endif</span></div>
<div class="line"><a name="l00703"></a><span class="lineno">  703</span>&#160; </div>
<div class="line"><a name="l00704"></a><span class="lineno">  704</span>&#160;  EIGEN_DEVICE_FUNC <span class="keywordtype">void</span> evalTo(Scalar* buffer)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00705"></a><span class="lineno">  705</span>&#160;   <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>Derived*<span class="keyword">&gt;</span>(<span class="keyword">this</span>)-&gt;<span class="keyword">template</span> evalProduct&lt;Unaligned&gt;(buffer);</div>
<div class="line"><a name="l00706"></a><span class="lineno">  706</span>&#160;  }</div>
<div class="line"><a name="l00707"></a><span class="lineno">  707</span>&#160; </div>
<div class="line"><a name="l00708"></a><span class="lineno">  708</span>&#160;<span class="preprocessor">#ifdef EIGEN_USE_THREADS</span></div>
<div class="line"><a name="l00709"></a><span class="lineno">  709</span>&#160;  <span class="keyword">template</span> &lt;<span class="keyword">typename</span> EvalToCallback&gt;</div>
<div class="line"><a name="l00710"></a><span class="lineno">  710</span>&#160;  <span class="keywordtype">void</span> evalToAsync(Scalar* buffer, EvalToCallback done)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00711"></a><span class="lineno">  711</span>&#160;    <span class="keyword">static_cast&lt;</span><span class="keyword">const </span>Derived*<span class="keyword">&gt;</span>(<span class="keyword">this</span>)</div>
<div class="line"><a name="l00712"></a><span class="lineno">  712</span>&#160;        -&gt;<span class="keyword">template</span> evalProductAsync&lt;EvalToCallback, Unaligned&gt;(buffer,</div>
<div class="line"><a name="l00713"></a><span class="lineno">  713</span>&#160;                                                               std::move(done));</div>
<div class="line"><a name="l00714"></a><span class="lineno">  714</span>&#160;  }</div>
<div class="line"><a name="l00715"></a><span class="lineno">  715</span>&#160;<span class="preprocessor">#endif  </span><span class="comment">// EIGEN_USE_THREADS</span></div>
<div class="line"><a name="l00716"></a><span class="lineno">  716</span>&#160; </div>
<div class="line"><a name="l00717"></a><span class="lineno">  717</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> lhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00718"></a><span class="lineno">  718</span>&#160;            <span class="keywordtype">bool</span> rhs_inner_dim_reordered, <span class="keywordtype">int</span> Alignment&gt;</div>
<div class="line"><a name="l00719"></a><span class="lineno">  719</span>&#160;  <span class="keywordtype">void</span> evalProductSequential(Scalar* buffer)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00720"></a><span class="lineno">  720</span>&#160;    <span class="keywordflow">if</span> (this-&gt;m_j_size == 1) {</div>
<div class="line"><a name="l00721"></a><span class="lineno">  721</span>&#160;      this-&gt;<span class="keyword">template</span> evalGemv&lt;lhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00722"></a><span class="lineno">  722</span>&#160;                              rhs_inner_dim_contiguous, rhs_inner_dim_reordered,</div>
<div class="line"><a name="l00723"></a><span class="lineno">  723</span>&#160;                              Alignment&gt;(buffer);</div>
<div class="line"><a name="l00724"></a><span class="lineno">  724</span>&#160;    } <span class="keywordflow">else</span> {</div>
<div class="line"><a name="l00725"></a><span class="lineno">  725</span>&#160;      this-&gt;<span class="keyword">template</span> evalGemm&lt;lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00726"></a><span class="lineno">  726</span>&#160;                              rhs_inner_dim_reordered, Alignment&gt;(buffer);</div>
<div class="line"><a name="l00727"></a><span class="lineno">  727</span>&#160;    }</div>
<div class="line"><a name="l00728"></a><span class="lineno">  728</span>&#160;  }</div>
<div class="line"><a name="l00729"></a><span class="lineno">  729</span>&#160; </div>
<div class="line"><a name="l00730"></a><span class="lineno">  730</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> lhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_reordered, <span class="keywordtype">int</span> Alignment&gt;</div>
<div class="line"><a name="l00731"></a><span class="lineno">  731</span>&#160;<span class="preprocessor">  #if !defined(EIGEN_HIPCC)</span></div>
<div class="line"><a name="l00732"></a><span class="lineno">  732</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00733"></a><span class="lineno">  733</span>&#160;<span class="preprocessor">  #endif</span></div>
<div class="line"><a name="l00734"></a><span class="lineno">  734</span>&#160;  <span class="keywordtype">void</span> evalGemv(Scalar* buffer)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00735"></a><span class="lineno">  735</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> rows = m_i_size;</div>
<div class="line"><a name="l00736"></a><span class="lineno">  736</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> cols = m_k_size;</div>
<div class="line"><a name="l00737"></a><span class="lineno">  737</span>&#160; </div>
<div class="line"><a name="l00738"></a><span class="lineno">  738</span>&#160;    <span class="keyword">typedef</span> std::remove_const_t&lt;typename EvalLeftArgType::Scalar&gt; LhsScalar;</div>
<div class="line"><a name="l00739"></a><span class="lineno">  739</span>&#160;    <span class="keyword">typedef</span> std::remove_const_t&lt;typename EvalRightArgType::Scalar&gt; RhsScalar;</div>
<div class="line"><a name="l00740"></a><span class="lineno">  740</span>&#160;    <span class="keyword">typedef</span> TensorEvaluator&lt;EvalLeftArgType, Device&gt; LeftEvaluator;</div>
<div class="line"><a name="l00741"></a><span class="lineno">  741</span>&#160;    <span class="keyword">typedef</span> TensorEvaluator&lt;EvalRightArgType, Device&gt; RightEvaluator;</div>
<div class="line"><a name="l00742"></a><span class="lineno">  742</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lhs_packet_size = internal::unpacket_traits&lt;typename LeftEvaluator::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00743"></a><span class="lineno">  743</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> rhs_packet_size = internal::unpacket_traits&lt;typename RightEvaluator::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00744"></a><span class="lineno">  744</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> lhs_alignment = LeftEvaluator::IsAligned ? <a class="codeRef" href="../group__enums.html#gga45fe06e29902b7a2773de05ba27b47a1ae12d0f8f869c40c76128260af2242bc8">Aligned</a> : <a class="codeRef" href="../group__enums.html#gga45fe06e29902b7a2773de05ba27b47a1a4e19dd09d5ff42295ba1d72d12a46686">Unaligned</a>;</div>
<div class="line"><a name="l00745"></a><span class="lineno">  745</span>&#160;    <span class="keyword">const</span> <span class="keywordtype">int</span> rhs_alignment = RightEvaluator::IsAligned ? <a class="codeRef" href="../group__enums.html#gga45fe06e29902b7a2773de05ba27b47a1ae12d0f8f869c40c76128260af2242bc8">Aligned</a> : <a class="codeRef" href="../group__enums.html#gga45fe06e29902b7a2773de05ba27b47a1a4e19dd09d5ff42295ba1d72d12a46686">Unaligned</a>;</div>
<div class="line"><a name="l00746"></a><span class="lineno">  746</span>&#160;    <span class="keyword">typedef</span> internal::TensorContractionInputMapper&lt;LhsScalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, internal::Lhs,</div>
<div class="line"><a name="l00747"></a><span class="lineno">  747</span>&#160;                                                   LeftEvaluator, left_nocontract_t,</div>
<div class="line"><a name="l00748"></a><span class="lineno">  748</span>&#160;                                                   contract_t, lhs_packet_size,</div>
<div class="line"><a name="l00749"></a><span class="lineno">  749</span>&#160;                                                   lhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00750"></a><span class="lineno">  750</span>&#160;                                                   <span class="keyword">false</span>, lhs_alignment&gt; LhsMapper;</div>
<div class="line"><a name="l00751"></a><span class="lineno">  751</span>&#160; </div>
<div class="line"><a name="l00752"></a><span class="lineno">  752</span>&#160;    <span class="keyword">typedef</span> internal::TensorContractionInputMapper&lt;RhsScalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, internal::Rhs,</div>
<div class="line"><a name="l00753"></a><span class="lineno">  753</span>&#160;                                                   RightEvaluator, right_nocontract_t,</div>
<div class="line"><a name="l00754"></a><span class="lineno">  754</span>&#160;                                                   contract_t, rhs_packet_size,</div>
<div class="line"><a name="l00755"></a><span class="lineno">  755</span>&#160;                                                   rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00756"></a><span class="lineno">  756</span>&#160;                                                   rhs_inner_dim_reordered, rhs_alignment&gt; RhsMapper;</div>
<div class="line"><a name="l00757"></a><span class="lineno">  757</span>&#160; </div>
<div class="line"><a name="l00758"></a><span class="lineno">  758</span>&#160;    LhsMapper lhs(m_leftImpl, m_left_nocontract_strides, m_i_strides,</div>
<div class="line"><a name="l00759"></a><span class="lineno">  759</span>&#160;                  m_left_contracting_strides, m_k_strides);</div>
<div class="line"><a name="l00760"></a><span class="lineno">  760</span>&#160;    RhsMapper rhs(m_rightImpl, m_right_nocontract_strides, m_j_strides,</div>
<div class="line"><a name="l00761"></a><span class="lineno">  761</span>&#160;                  m_right_contracting_strides, m_k_strides);</div>
<div class="line"><a name="l00762"></a><span class="lineno">  762</span>&#160; </div>
<div class="line"><a name="l00763"></a><span class="lineno">  763</span>&#160;    <span class="keyword">const</span> Scalar alpha(1);</div>
<div class="line"><a name="l00764"></a><span class="lineno">  764</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> resIncr(1);</div>
<div class="line"><a name="l00765"></a><span class="lineno">  765</span>&#160; </div>
<div class="line"><a name="l00766"></a><span class="lineno">  766</span>&#160;    <span class="comment">// zero out the result buffer (which must be of size at least rows * sizeof(Scalar)</span></div>
<div class="line"><a name="l00767"></a><span class="lineno">  767</span>&#160;    m_device.fill(buffer, buffer + rows, Scalar(0));</div>
<div class="line"><a name="l00768"></a><span class="lineno">  768</span>&#160; </div>
<div class="line"><a name="l00769"></a><span class="lineno">  769</span>&#160;    internal::general_matrix_vector_product&lt;Index,LhsScalar,LhsMapper,ColMajor,false,RhsScalar,RhsMapper,false&gt;::run(</div>
<div class="line"><a name="l00770"></a><span class="lineno">  770</span>&#160;        rows, cols, lhs, rhs,</div>
<div class="line"><a name="l00771"></a><span class="lineno">  771</span>&#160;        buffer, resIncr, alpha);</div>
<div class="line"><a name="l00772"></a><span class="lineno">  772</span>&#160; </div>
<div class="line"><a name="l00773"></a><span class="lineno">  773</span>&#160;    <span class="keyword">typedef</span> internal::blas_data_mapper&lt;Scalar, Index, ColMajor&gt; OutputMapper;</div>
<div class="line"><a name="l00774"></a><span class="lineno">  774</span>&#160;    m_output_kernel(OutputMapper(buffer, rows), m_tensor_contraction_params,</div>
<div class="line"><a name="l00775"></a><span class="lineno">  775</span>&#160;                    <span class="keyword">static_cast&lt;</span><a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a><span class="keyword">&gt;</span>(0), <span class="keyword">static_cast&lt;</span><a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a><span class="keyword">&gt;</span>(0), rows,</div>
<div class="line"><a name="l00776"></a><span class="lineno">  776</span>&#160;                    <span class="keyword">static_cast&lt;</span><a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a><span class="keyword">&gt;</span>(1));</div>
<div class="line"><a name="l00777"></a><span class="lineno">  777</span>&#160;  }</div>
<div class="line"><a name="l00778"></a><span class="lineno">  778</span>&#160; </div>
<div class="line"><a name="l00779"></a><span class="lineno">  779</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> lhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_reordered, <span class="keywordtype">int</span> Alignment&gt;</div>
<div class="line"><a name="l00780"></a><span class="lineno">  780</span>&#160;<span class="preprocessor">  #if !defined(EIGEN_HIPCC)</span></div>
<div class="line"><a name="l00781"></a><span class="lineno">  781</span>&#160;  EIGEN_DEVICE_FUNC</div>
<div class="line"><a name="l00782"></a><span class="lineno">  782</span>&#160;<span class="preprocessor">  #endif</span></div>
<div class="line"><a name="l00783"></a><span class="lineno">  783</span>&#160;  <span class="keywordtype">void</span> evalGemm(Scalar* buffer)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00784"></a><span class="lineno">  784</span>&#160;    <span class="comment">// columns in left side, rows in right side</span></div>
<div class="line"><a name="l00785"></a><span class="lineno">  785</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k = this-&gt;m_k_size;</div>
<div class="line"><a name="l00786"></a><span class="lineno">  786</span>&#160;    this-&gt;<span class="keyword">template</span> evalGemmPartial&lt;lhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00787"></a><span class="lineno">  787</span>&#160;                                   rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00788"></a><span class="lineno">  788</span>&#160;                                   rhs_inner_dim_reordered,</div>
<div class="line"><a name="l00789"></a><span class="lineno">  789</span>&#160;                                   Alignment, <span class="keyword">true</span>&gt;(buffer, 0, k, 1);</div>
<div class="line"><a name="l00790"></a><span class="lineno">  790</span>&#160;  }</div>
<div class="line"><a name="l00791"></a><span class="lineno">  791</span>&#160; </div>
<div class="line"><a name="l00792"></a><span class="lineno">  792</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> lhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00793"></a><span class="lineno">  793</span>&#160;      <span class="keywordtype">bool</span> rhs_inner_dim_reordered, <span class="keywordtype">int</span> Alignment&gt;</div>
<div class="line"><a name="l00794"></a><span class="lineno">  794</span>&#160;  EIGEN_DEVICE_FUNC <span class="keywordtype">void</span> evalGemmPartialWithoutOutputKernel(</div>
<div class="line"><a name="l00795"></a><span class="lineno">  795</span>&#160;      Scalar* buffer, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k_start, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k_end, <span class="keywordtype">int</span> num_threads)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00796"></a><span class="lineno">  796</span>&#160;    evalGemmPartial&lt;lhs_inner_dim_contiguous, rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00797"></a><span class="lineno">  797</span>&#160;                    rhs_inner_dim_reordered, Alignment,</div>
<div class="line"><a name="l00798"></a><span class="lineno">  798</span>&#160;        <span class="comment">/*use_output_kernel*/</span> <span class="keyword">false</span>&gt;(buffer, k_start, k_end,</div>
<div class="line"><a name="l00799"></a><span class="lineno">  799</span>&#160;                                     num_threads);</div>
<div class="line"><a name="l00800"></a><span class="lineno">  800</span>&#160;  }</div>
<div class="line"><a name="l00801"></a><span class="lineno">  801</span>&#160; </div>
<div class="line"><a name="l00802"></a><span class="lineno">  802</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">bool</span> lhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_contiguous, <span class="keywordtype">bool</span> rhs_inner_dim_reordered, <span class="keywordtype">int</span> Alignment, <span class="keywordtype">bool</span> use_output_kernel&gt;</div>
<div class="line"><a name="l00803"></a><span class="lineno">  803</span>&#160;  EIGEN_DEVICE_FUNC <span class="keywordtype">void</span> evalGemmPartial(Scalar* buffer, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k_start, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k_end, <span class="keywordtype">int</span> num_threads)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00804"></a><span class="lineno">  804</span>&#160;    eigen_assert(k_end &gt;= k_start &amp;&amp; k_start &gt;= 0 &amp;&amp; k_end &lt;= this-&gt;m_k_size);</div>
<div class="line"><a name="l00805"></a><span class="lineno">  805</span>&#160;    <span class="comment">// columns in slice on left side, rows on right side</span></div>
<div class="line"><a name="l00806"></a><span class="lineno">  806</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k_slice = k_end - k_start;</div>
<div class="line"><a name="l00807"></a><span class="lineno">  807</span>&#160; </div>
<div class="line"><a name="l00808"></a><span class="lineno">  808</span>&#160;    <span class="comment">// rows in left side</span></div>
<div class="line"><a name="l00809"></a><span class="lineno">  809</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> m = this-&gt;m_i_size;</div>
<div class="line"><a name="l00810"></a><span class="lineno">  810</span>&#160; </div>
<div class="line"><a name="l00811"></a><span class="lineno">  811</span>&#160;    <span class="comment">// columns in right side</span></div>
<div class="line"><a name="l00812"></a><span class="lineno">  812</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> n = this-&gt;m_j_size;</div>
<div class="line"><a name="l00813"></a><span class="lineno">  813</span>&#160; </div>
<div class="line"><a name="l00814"></a><span class="lineno">  814</span>&#160;    <span class="comment">// define data mappers for Lhs and Rhs</span></div>
<div class="line"><a name="l00815"></a><span class="lineno">  815</span>&#160;    <span class="keyword">typedef</span> std::remove_const_t&lt;typename EvalLeftArgType::Scalar&gt; LhsScalar;</div>
<div class="line"><a name="l00816"></a><span class="lineno">  816</span>&#160;    <span class="keyword">typedef</span> std::remove_const_t&lt;typename EvalRightArgType::Scalar&gt; RhsScalar;</div>
<div class="line"><a name="l00817"></a><span class="lineno">  817</span>&#160; </div>
<div class="line"><a name="l00818"></a><span class="lineno">  818</span>&#160;    <span class="keyword">typedef</span> TensorEvaluator&lt;EvalLeftArgType, Device&gt; LeftEvaluator;</div>
<div class="line"><a name="l00819"></a><span class="lineno">  819</span>&#160;    <span class="keyword">typedef</span> TensorEvaluator&lt;EvalRightArgType, Device&gt; RightEvaluator;</div>
<div class="line"><a name="l00820"></a><span class="lineno">  820</span>&#160; </div>
<div class="line"><a name="l00821"></a><span class="lineno">  821</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> lhs_packet_size = internal::unpacket_traits&lt;typename LeftEvaluator::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00822"></a><span class="lineno">  822</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> rhs_packet_size = internal::unpacket_traits&lt;typename RightEvaluator::PacketReturnType&gt;::size;</div>
<div class="line"><a name="l00823"></a><span class="lineno">  823</span>&#160; </div>
<div class="line"><a name="l00824"></a><span class="lineno">  824</span>&#160;    <span class="keyword">typedef</span> internal::TensorContractionInputMapper&lt;LhsScalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, internal::Lhs,</div>
<div class="line"><a name="l00825"></a><span class="lineno">  825</span>&#160;                                                   LeftEvaluator, left_nocontract_t,</div>
<div class="line"><a name="l00826"></a><span class="lineno">  826</span>&#160;                                                   contract_t, lhs_packet_size,</div>
<div class="line"><a name="l00827"></a><span class="lineno">  827</span>&#160;                                                   lhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00828"></a><span class="lineno">  828</span>&#160;                                                   <span class="keyword">false</span>, <a class="codeRef" href="../group__enums.html#gga45fe06e29902b7a2773de05ba27b47a1a4e19dd09d5ff42295ba1d72d12a46686">Unaligned</a>&gt; LhsMapper;</div>
<div class="line"><a name="l00829"></a><span class="lineno">  829</span>&#160; </div>
<div class="line"><a name="l00830"></a><span class="lineno">  830</span>&#160;    <span class="keyword">typedef</span> internal::TensorContractionInputMapper&lt;RhsScalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, internal::Rhs,</div>
<div class="line"><a name="l00831"></a><span class="lineno">  831</span>&#160;                                                   RightEvaluator, right_nocontract_t,</div>
<div class="line"><a name="l00832"></a><span class="lineno">  832</span>&#160;                                                   contract_t, rhs_packet_size,</div>
<div class="line"><a name="l00833"></a><span class="lineno">  833</span>&#160;                                                   rhs_inner_dim_contiguous,</div>
<div class="line"><a name="l00834"></a><span class="lineno">  834</span>&#160;                                                   rhs_inner_dim_reordered, <a class="codeRef" href="../group__enums.html#gga45fe06e29902b7a2773de05ba27b47a1a4e19dd09d5ff42295ba1d72d12a46686">Unaligned</a>&gt; RhsMapper;</div>
<div class="line"><a name="l00835"></a><span class="lineno">  835</span>&#160; </div>
<div class="line"><a name="l00836"></a><span class="lineno">  836</span>&#160;    <span class="keyword">typedef</span> internal::blas_data_mapper&lt;Scalar, Index, ColMajor&gt; OutputMapper;</div>
<div class="line"><a name="l00837"></a><span class="lineno">  837</span>&#160; </div>
<div class="line"><a name="l00838"></a><span class="lineno">  838</span>&#160;    <span class="keyword">typedef</span> internal::TensorContractionKernel&lt;</div>
<div class="line"><a name="l00839"></a><span class="lineno">  839</span>&#160;        Scalar, LhsScalar, RhsScalar, <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, OutputMapper, LhsMapper, RhsMapper&gt;</div>
<div class="line"><a name="l00840"></a><span class="lineno">  840</span>&#160;        TensorContractionKernel;</div>
<div class="line"><a name="l00841"></a><span class="lineno">  841</span>&#160; </div>
<div class="line"><a name="l00842"></a><span class="lineno">  842</span>&#160;    <span class="comment">// initialize data mappers</span></div>
<div class="line"><a name="l00843"></a><span class="lineno">  843</span>&#160;    LhsMapper lhs(this-&gt;m_leftImpl, this-&gt;m_left_nocontract_strides, this-&gt;m_i_strides,</div>
<div class="line"><a name="l00844"></a><span class="lineno">  844</span>&#160;                  this-&gt;m_left_contracting_strides, this-&gt;m_k_strides);</div>
<div class="line"><a name="l00845"></a><span class="lineno">  845</span>&#160; </div>
<div class="line"><a name="l00846"></a><span class="lineno">  846</span>&#160;    RhsMapper rhs(this-&gt;m_rightImpl, this-&gt;m_right_nocontract_strides, this-&gt;m_j_strides,</div>
<div class="line"><a name="l00847"></a><span class="lineno">  847</span>&#160;                  this-&gt;m_right_contracting_strides, this-&gt;m_k_strides);</div>
<div class="line"><a name="l00848"></a><span class="lineno">  848</span>&#160; </div>
<div class="line"><a name="l00849"></a><span class="lineno">  849</span>&#160;    OutputMapper output(buffer, m);</div>
<div class="line"><a name="l00850"></a><span class="lineno">  850</span>&#160; </div>
<div class="line"><a name="l00851"></a><span class="lineno">  851</span>&#160;    <span class="comment">// Sizes of the blocks to load in cache. See the Goto paper for details.</span></div>
<div class="line"><a name="l00852"></a><span class="lineno">  852</span>&#160;    internal::TensorContractionBlocking&lt;Scalar, LhsScalar, RhsScalar,</div>
<div class="line"><a name="l00853"></a><span class="lineno">  853</span>&#160;                                        <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, internal::ShardByCol&gt;</div>
<div class="line"><a name="l00854"></a><span class="lineno">  854</span>&#160;        blocking(k_slice, m, n, num_threads);</div>
<div class="line"><a name="l00855"></a><span class="lineno">  855</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> kc = blocking.kc();</div>
<div class="line"><a name="l00856"></a><span class="lineno">  856</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> mc = numext::mini(m, blocking.mc());</div>
<div class="line"><a name="l00857"></a><span class="lineno">  857</span>&#160;    <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> nc = numext::mini(n, blocking.nc());</div>
<div class="line"><a name="l00858"></a><span class="lineno">  858</span>&#160; </div>
<div class="line"><a name="l00859"></a><span class="lineno">  859</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorContractionKernel::LhsBlock LhsBlock;</div>
<div class="line"><a name="l00860"></a><span class="lineno">  860</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorContractionKernel::RhsBlock RhsBlock;</div>
<div class="line"><a name="l00861"></a><span class="lineno">  861</span>&#160; </div>
<div class="line"><a name="l00862"></a><span class="lineno">  862</span>&#160;    LhsBlock blockA;</div>
<div class="line"><a name="l00863"></a><span class="lineno">  863</span>&#160;    RhsBlock blockB;</div>
<div class="line"><a name="l00864"></a><span class="lineno">  864</span>&#160; </div>
<div class="line"><a name="l00865"></a><span class="lineno">  865</span>&#160;    TensorContractionKernel kernel(m, k_slice, n, mc, kc, nc);</div>
<div class="line"><a name="l00866"></a><span class="lineno">  866</span>&#160; </div>
<div class="line"><a name="l00867"></a><span class="lineno">  867</span>&#160;    <span class="keyword">typedef</span> <span class="keyword">typename</span> TensorContractionKernel::BlockMemHandle BlockMemHandle;</div>
<div class="line"><a name="l00868"></a><span class="lineno">  868</span>&#160;    <span class="keyword">const</span> BlockMemHandle packed_mem =</div>
<div class="line"><a name="l00869"></a><span class="lineno">  869</span>&#160;        kernel.allocate(this-&gt;m_device, &amp;blockA, &amp;blockB);</div>
<div class="line"><a name="l00870"></a><span class="lineno">  870</span>&#160; </div>
<div class="line"><a name="l00871"></a><span class="lineno">  871</span>&#160;    <span class="comment">// If a contraction kernel does not support beta, explicitly initialize</span></div>
<div class="line"><a name="l00872"></a><span class="lineno">  872</span>&#160;    <span class="comment">// output buffer with zeroes.</span></div>
<div class="line"><a name="l00873"></a><span class="lineno">  873</span>&#160;    <span class="keywordflow">if</span> (!TensorContractionKernel::HasBeta) {</div>
<div class="line"><a name="l00874"></a><span class="lineno">  874</span>&#160;      this-&gt;m_device.fill(buffer, buffer + m * n, Scalar(0));</div>
<div class="line"><a name="l00875"></a><span class="lineno">  875</span>&#160;    }</div>
<div class="line"><a name="l00876"></a><span class="lineno">  876</span>&#160; </div>
<div class="line"><a name="l00877"></a><span class="lineno">  877</span>&#160;    <span class="keywordflow">for</span>(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> i2=0; i2&lt;m; i2+=mc)</div>
<div class="line"><a name="l00878"></a><span class="lineno">  878</span>&#160;    {</div>
<div class="line"><a name="l00879"></a><span class="lineno">  879</span>&#160;      <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> actual_mc = numext::mini(i2+mc,m)-i2;</div>
<div class="line"><a name="l00880"></a><span class="lineno">  880</span>&#160;      <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> k2 = k_start; k2 &lt; k_end; k2 += kc) {</div>
<div class="line"><a name="l00881"></a><span class="lineno">  881</span>&#160;        <span class="comment">// make sure we don&#39;t overshoot right edge of left matrix, then pack vertical panel</span></div>
<div class="line"><a name="l00882"></a><span class="lineno">  882</span>&#160;        <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> actual_kc = numext::mini(k2 + kc, k_end) - k2;</div>
<div class="line"><a name="l00883"></a><span class="lineno">  883</span>&#160;        kernel.packLhs(&amp;blockA, lhs.getSubMapper(i2, k2), actual_kc, actual_mc);</div>
<div class="line"><a name="l00884"></a><span class="lineno">  884</span>&#160; </div>
<div class="line"><a name="l00885"></a><span class="lineno">  885</span>&#160;        <span class="comment">// If kernel supports beta, there is no need to initialize output</span></div>
<div class="line"><a name="l00886"></a><span class="lineno">  886</span>&#160;        <span class="comment">// buffer with zeroes.</span></div>
<div class="line"><a name="l00887"></a><span class="lineno">  887</span>&#160;        <span class="keyword">const</span> Scalar alpha = Scalar(1);</div>
<div class="line"><a name="l00888"></a><span class="lineno">  888</span>&#160;        <span class="keyword">const</span> Scalar beta = (TensorContractionKernel::HasBeta &amp;&amp; k2 == k_start)</div>
<div class="line"><a name="l00889"></a><span class="lineno">  889</span>&#160;                                ? Scalar(0)</div>
<div class="line"><a name="l00890"></a><span class="lineno">  890</span>&#160;                                : Scalar(1);</div>
<div class="line"><a name="l00891"></a><span class="lineno">  891</span>&#160; </div>
<div class="line"><a name="l00892"></a><span class="lineno">  892</span>&#160;        <span class="comment">// series of horizontal blocks</span></div>
<div class="line"><a name="l00893"></a><span class="lineno">  893</span>&#160;        <span class="keywordflow">for</span> (<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> j2 = 0; j2 &lt; n; j2 += nc) {</div>
<div class="line"><a name="l00894"></a><span class="lineno">  894</span>&#160;          <span class="comment">// make sure we don&#39;t overshoot right edge of right matrix, then pack block</span></div>
<div class="line"><a name="l00895"></a><span class="lineno">  895</span>&#160;          <span class="keyword">const</span> <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> actual_nc = numext::mini(j2 + nc, n) - j2;</div>
<div class="line"><a name="l00896"></a><span class="lineno">  896</span>&#160;          kernel.packRhs(&amp;blockB, rhs.getSubMapper(k2, j2), actual_kc,</div>
<div class="line"><a name="l00897"></a><span class="lineno">  897</span>&#160;                         actual_nc);</div>
<div class="line"><a name="l00898"></a><span class="lineno">  898</span>&#160; </div>
<div class="line"><a name="l00899"></a><span class="lineno">  899</span>&#160;          <span class="comment">// call gebp (matrix kernel)</span></div>
<div class="line"><a name="l00900"></a><span class="lineno">  900</span>&#160;          <span class="comment">// The parameters here are copied from Eigen&#39;s GEMM implementation</span></div>
<div class="line"><a name="l00901"></a><span class="lineno">  901</span>&#160;          <span class="keyword">const</span> OutputMapper output_mapper = output.getSubMapper(i2, j2);</div>
<div class="line"><a name="l00902"></a><span class="lineno">  902</span>&#160;          kernel.invoke(output_mapper, blockA, blockB, actual_mc, actual_kc,</div>
<div class="line"><a name="l00903"></a><span class="lineno">  903</span>&#160;                        actual_nc, alpha, beta);</div>
<div class="line"><a name="l00904"></a><span class="lineno">  904</span>&#160; </div>
<div class="line"><a name="l00905"></a><span class="lineno">  905</span>&#160;          <span class="comment">// We are done with this [i2, j2] output block.</span></div>
<div class="line"><a name="l00906"></a><span class="lineno">  906</span>&#160;          <span class="keywordflow">if</span> (use_output_kernel &amp;&amp; k2 + kc &gt;= k_end) {</div>
<div class="line"><a name="l00907"></a><span class="lineno">  907</span>&#160;            m_output_kernel(output_mapper, m_tensor_contraction_params, i2, j2,</div>
<div class="line"><a name="l00908"></a><span class="lineno">  908</span>&#160;                            actual_mc, actual_nc);</div>
<div class="line"><a name="l00909"></a><span class="lineno">  909</span>&#160;          }</div>
<div class="line"><a name="l00910"></a><span class="lineno">  910</span>&#160;        }</div>
<div class="line"><a name="l00911"></a><span class="lineno">  911</span>&#160;      }</div>
<div class="line"><a name="l00912"></a><span class="lineno">  912</span>&#160;    }</div>
<div class="line"><a name="l00913"></a><span class="lineno">  913</span>&#160; </div>
<div class="line"><a name="l00914"></a><span class="lineno">  914</span>&#160;    kernel.deallocate(this-&gt;m_device, packed_mem);</div>
<div class="line"><a name="l00915"></a><span class="lineno">  915</span>&#160;  }</div>
<div class="line"><a name="l00916"></a><span class="lineno">  916</span>&#160; </div>
<div class="line"><a name="l00917"></a><span class="lineno">  917</span>&#160;  EIGEN_STRONG_INLINE <span class="keywordtype">void</span> cleanup() {</div>
<div class="line"><a name="l00918"></a><span class="lineno">  918</span>&#160;    m_leftImpl.cleanup();</div>
<div class="line"><a name="l00919"></a><span class="lineno">  919</span>&#160;    m_rightImpl.cleanup();</div>
<div class="line"><a name="l00920"></a><span class="lineno">  920</span>&#160; </div>
<div class="line"><a name="l00921"></a><span class="lineno">  921</span>&#160;    <span class="keywordflow">if</span> (m_result != NULL) {</div>
<div class="line"><a name="l00922"></a><span class="lineno">  922</span>&#160;      m_device.deallocate(m_result);</div>
<div class="line"><a name="l00923"></a><span class="lineno">  923</span>&#160;      m_result = NULL;</div>
<div class="line"><a name="l00924"></a><span class="lineno">  924</span>&#160;    }</div>
<div class="line"><a name="l00925"></a><span class="lineno">  925</span>&#160;  }</div>
<div class="line"><a name="l00926"></a><span class="lineno">  926</span>&#160; </div>
<div class="line"><a name="l00927"></a><span class="lineno">  927</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> index)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00928"></a><span class="lineno">  928</span>&#160;    <span class="keywordflow">return</span> m_result[index];</div>
<div class="line"><a name="l00929"></a><span class="lineno">  929</span>&#160;  }</div>
<div class="line"><a name="l00930"></a><span class="lineno">  930</span>&#160; </div>
<div class="line"><a name="l00931"></a><span class="lineno">  931</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(<span class="keywordtype">bool</span>)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00932"></a><span class="lineno">  932</span>&#160;    <span class="keywordflow">return</span> TensorOpCost(<span class="keyword">sizeof</span>(CoeffReturnType), 0, 0);</div>
<div class="line"><a name="l00933"></a><span class="lineno">  933</span>&#160;  }</div>
<div class="line"><a name="l00934"></a><span class="lineno">  934</span>&#160; </div>
<div class="line"><a name="l00935"></a><span class="lineno">  935</span>&#160;  <span class="keyword">template</span>&lt;<span class="keywordtype">int</span> LoadMode&gt;</div>
<div class="line"><a name="l00936"></a><span class="lineno">  936</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> index)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l00937"></a><span class="lineno">  937</span>&#160;    <span class="keywordflow">return</span> internal::ploadt&lt;PacketReturnType, LoadMode&gt;(m_result + index);</div>
<div class="line"><a name="l00938"></a><span class="lineno">  938</span>&#160;  }</div>
<div class="line"><a name="l00939"></a><span class="lineno">  939</span>&#160; </div>
<div class="line"><a name="l00940"></a><span class="lineno">  940</span>&#160;  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data()<span class="keyword"> const </span>{ <span class="keywordflow">return</span> m_result; }</div>
<div class="line"><a name="l00941"></a><span class="lineno">  941</span>&#160; </div>
<div class="line"><a name="l00942"></a><span class="lineno">  942</span>&#160;<span class="keyword">protected</span>:</div>
<div class="line"><a name="l00943"></a><span class="lineno">  943</span>&#160;  Dimensions m_dimensions;</div>
<div class="line"><a name="l00944"></a><span class="lineno">  944</span>&#160; </div>
<div class="line"><a name="l00945"></a><span class="lineno">  945</span>&#160;  contract_t m_k_strides;</div>
<div class="line"><a name="l00946"></a><span class="lineno">  946</span>&#160;  contract_t m_left_contracting_strides;</div>
<div class="line"><a name="l00947"></a><span class="lineno">  947</span>&#160;  contract_t m_right_contracting_strides;</div>
<div class="line"><a name="l00948"></a><span class="lineno">  948</span>&#160; </div>
<div class="line"><a name="l00949"></a><span class="lineno">  949</span>&#160;  <span class="keywordtype">bool</span> m_lhs_inner_dim_contiguous;</div>
<div class="line"><a name="l00950"></a><span class="lineno">  950</span>&#160;  <span class="keywordtype">bool</span> m_rhs_inner_dim_contiguous;</div>
<div class="line"><a name="l00951"></a><span class="lineno">  951</span>&#160;  <span class="keywordtype">bool</span> m_rhs_inner_dim_reordered;</div>
<div class="line"><a name="l00952"></a><span class="lineno">  952</span>&#160; </div>
<div class="line"><a name="l00953"></a><span class="lineno">  953</span>&#160;  left_nocontract_t m_i_strides;</div>
<div class="line"><a name="l00954"></a><span class="lineno">  954</span>&#160;  right_nocontract_t m_j_strides;</div>
<div class="line"><a name="l00955"></a><span class="lineno">  955</span>&#160;  left_nocontract_t m_left_nocontract_strides;</div>
<div class="line"><a name="l00956"></a><span class="lineno">  956</span>&#160;  right_nocontract_t m_right_nocontract_strides;</div>
<div class="line"><a name="l00957"></a><span class="lineno">  957</span>&#160; </div>
<div class="line"><a name="l00958"></a><span class="lineno">  958</span>&#160;  <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> m_i_size;</div>
<div class="line"><a name="l00959"></a><span class="lineno">  959</span>&#160;  <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> m_j_size;</div>
<div class="line"><a name="l00960"></a><span class="lineno">  960</span>&#160;  <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a> m_k_size;</div>
<div class="line"><a name="l00961"></a><span class="lineno">  961</span>&#160; </div>
<div class="line"><a name="l00962"></a><span class="lineno">  962</span>&#160;  TensorContractionParams m_tensor_contraction_params;</div>
<div class="line"><a name="l00963"></a><span class="lineno">  963</span>&#160; </div>
<div class="line"><a name="l00964"></a><span class="lineno">  964</span>&#160;  TensorEvaluator&lt;EvalLeftArgType, Device&gt; m_leftImpl;</div>
<div class="line"><a name="l00965"></a><span class="lineno">  965</span>&#160;  TensorEvaluator&lt;EvalRightArgType, Device&gt; m_rightImpl;</div>
<div class="line"><a name="l00966"></a><span class="lineno">  966</span>&#160;  <span class="keyword">const</span> Device EIGEN_DEVICE_REF m_device;</div>
<div class="line"><a name="l00967"></a><span class="lineno">  967</span>&#160;  OutputKernelType m_output_kernel;</div>
<div class="line"><a name="l00968"></a><span class="lineno">  968</span>&#160;  EvaluatorPointerType m_result;</div>
<div class="line"><a name="l00969"></a><span class="lineno">  969</span>&#160;};</div>
<div class="line"><a name="l00970"></a><span class="lineno">  970</span>&#160; </div>
<div class="line"><a name="l00971"></a><span class="lineno">  971</span>&#160; </div>
<div class="line"><a name="l00972"></a><span class="lineno">  972</span>&#160;<span class="comment">// evaluator for default device</span></div>
<div class="line"><a name="l00973"></a><span class="lineno">  973</span>&#160;<span class="keyword">template</span>&lt;<span class="keyword">typename</span> Indices, <span class="keyword">typename</span> LeftArgType, <span class="keyword">typename</span> RightArgType, <span class="keyword">typename</span> OutputKernelType, <span class="keyword">typename</span> Device&gt;</div>
<div class="line"><a name="l00974"></a><span class="lineno">  974</span>&#160;<span class="keyword">struct </span>TensorEvaluator&lt;const TensorContractionOp&lt;Indices, LeftArgType, RightArgType, OutputKernelType&gt;, Device&gt; :</div>
<div class="line"><a name="l00975"></a><span class="lineno">  975</span>&#160;    <span class="keyword">public</span> TensorContractionEvaluatorBase&lt;</div>
<div class="line"><a name="l00976"></a><span class="lineno">  976</span>&#160;      TensorEvaluator&lt;const TensorContractionOp&lt;Indices, LeftArgType, RightArgType, OutputKernelType&gt;, Device&gt; &gt; {</div>
<div class="line"><a name="l00977"></a><span class="lineno">  977</span>&#160;  <span class="keyword">typedef</span> TensorEvaluator&lt;const TensorContractionOp&lt;Indices, LeftArgType, RightArgType, OutputKernelType&gt;, Device&gt; Self;</div>
<div class="line"><a name="l00978"></a><span class="lineno">  978</span>&#160;  <span class="keyword">typedef</span> TensorContractionEvaluatorBase&lt;Self&gt; Base;</div>
<div class="line"><a name="l00979"></a><span class="lineno">  979</span>&#160; </div>
<div class="line"><a name="l00980"></a><span class="lineno">  980</span>&#160;  <span class="keyword">typedef</span> TensorContractionOp&lt;Indices, LeftArgType, RightArgType, OutputKernelType&gt; XprType;</div>
<div class="line"><a name="l00981"></a><span class="lineno">  981</span>&#160;  <span class="keyword">typedef</span> std::remove_const_t&lt;typename XprType::Scalar&gt; Scalar;</div>
<div class="line"><a name="l00982"></a><span class="lineno">  982</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::Index <a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>;</div>
<div class="line"><a name="l00983"></a><span class="lineno">  983</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> XprType::CoeffReturnType CoeffReturnType;</div>
<div class="line"><a name="l00984"></a><span class="lineno">  984</span>&#160;  <span class="keyword">typedef</span> <span class="keyword">typename</span> PacketType&lt;CoeffReturnType, Device&gt;::type PacketReturnType;</div>
<div class="line"><a name="l00985"></a><span class="lineno">  985</span>&#160; </div>
<div class="line"><a name="l00986"></a><span class="lineno">  986</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> Layout = TensorEvaluator&lt;LeftArgType, Device&gt;::Layout;</div>
<div class="line"><a name="l00987"></a><span class="lineno">  987</span>&#160; </div>
<div class="line"><a name="l00988"></a><span class="lineno">  988</span>&#160;  <span class="comment">// Most of the code is assuming that both input tensors are ColMajor. If the</span></div>
<div class="line"><a name="l00989"></a><span class="lineno">  989</span>&#160;  <span class="comment">// inputs are RowMajor, we will &quot;cheat&quot; by swapping the LHS and RHS:</span></div>
<div class="line"><a name="l00990"></a><span class="lineno">  990</span>&#160;  <span class="comment">// If we want to compute A * B = C, where A is LHS and B is RHS, the code</span></div>
<div class="line"><a name="l00991"></a><span class="lineno">  991</span>&#160;  <span class="comment">// will pretend B is LHS and A is RHS.</span></div>
<div class="line"><a name="l00992"></a><span class="lineno">  992</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;Layout == static_cast&lt;int&gt;(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>), LeftArgType, RightArgType&gt; EvalLeftArgType;</div>
<div class="line"><a name="l00993"></a><span class="lineno">  993</span>&#160;  <span class="keyword">typedef</span> std::conditional_t&lt;Layout == static_cast&lt;int&gt;(<a class="codeRef" href="../group__enums.html#ggaacded1a18ae58b0f554751f6cdf9eb13a0103672ae41005ab03b4176c765afd62">ColMajor</a>), RightArgType, LeftArgType&gt; EvalRightArgType;</div>
<div class="line"><a name="l00994"></a><span class="lineno">  994</span>&#160; </div>
<div class="line"><a name="l00995"></a><span class="lineno">  995</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> LDims =</div>
<div class="line"><a name="l00996"></a><span class="lineno">  996</span>&#160;      internal::array_size&lt;typename TensorEvaluator&lt;EvalLeftArgType, Device&gt;::Dimensions&gt;::value;</div>
<div class="line"><a name="l00997"></a><span class="lineno">  997</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> RDims =</div>
<div class="line"><a name="l00998"></a><span class="lineno">  998</span>&#160;      internal::array_size&lt;typename TensorEvaluator&lt;EvalRightArgType, Device&gt;::Dimensions&gt;::value;</div>
<div class="line"><a name="l00999"></a><span class="lineno">  999</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> ContractDims = internal::array_size&lt;Indices&gt;::value;</div>
<div class="line"><a name="l01000"></a><span class="lineno"> 1000</span>&#160; </div>
<div class="line"><a name="l01001"></a><span class="lineno"> 1001</span>&#160;  <span class="keyword">typedef</span> array&lt;Index, ContractDims&gt; contract_t;</div>
<div class="line"><a name="l01002"></a><span class="lineno"> 1002</span>&#160;  <span class="keyword">typedef</span> array&lt;<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, LDims - ContractDims&gt; left_nocontract_t;</div>
<div class="line"><a name="l01003"></a><span class="lineno"> 1003</span>&#160;  <span class="keyword">typedef</span> array&lt;<a class="codeRef" href="../namespaceEigen.html#a62e77e0933482dafde8fe197d9a2cfde">Index</a>, RDims - ContractDims&gt; right_nocontract_t;</div>
<div class="line"><a name="l01004"></a><span class="lineno"> 1004</span>&#160; </div>
<div class="line"><a name="l01005"></a><span class="lineno"> 1005</span>&#160;  <span class="keyword">static</span> constexpr <span class="keywordtype">int</span> NumDims = LDims + RDims - 2 * ContractDims;</div>
<div class="line"><a name="l01006"></a><span class="lineno"> 1006</span>&#160; </div>
<div class="line"><a name="l01007"></a><span class="lineno"> 1007</span>&#160;  <span class="comment">// Could we use NumDimensions here?</span></div>
<div class="line"><a name="l01008"></a><span class="lineno"> 1008</span>&#160;  <span class="keyword">typedef</span> DSizes&lt;Index, NumDims&gt; Dimensions;</div>
<div class="line"><a name="l01009"></a><span class="lineno"> 1009</span>&#160; </div>
<div class="line"><a name="l01010"></a><span class="lineno"> 1010</span>&#160;  TensorEvaluator(<span class="keyword">const</span> XprType&amp; op, <span class="keyword">const</span> Device&amp; device) :</div>
<div class="line"><a name="l01011"></a><span class="lineno"> 1011</span>&#160;      Base(op, device) { }</div>
<div class="line"><a name="l01012"></a><span class="lineno"> 1012</span>&#160; </div>
<div class="line"><a name="l01013"></a><span class="lineno"> 1013</span>&#160;  <span class="keyword">template</span> &lt;<span class="keywordtype">int</span> Alignment&gt;</div>
<div class="line"><a name="l01014"></a><span class="lineno"> 1014</span>&#160;  <span class="keywordtype">void</span> evalProduct(Scalar* buffer)<span class="keyword"> const </span>{</div>
<div class="line"><a name="l01015"></a><span class="lineno"> 1015</span>&#160;    TENSOR_CONTRACTION_DISPATCH(this-&gt;<span class="keyword">template</span> evalProductSequential, Alignment, (buffer));</div>
<div class="line"><a name="l01016"></a><span class="lineno"> 1016</span>&#160;  }</div>
<div class="line"><a name="l01017"></a><span class="lineno"> 1017</span>&#160;};</div>
<div class="line"><a name="l01018"></a><span class="lineno"> 1018</span>&#160; </div>
<div class="line"><a name="l01019"></a><span class="lineno"> 1019</span>&#160;} <span class="comment">// end namespace Eigen</span></div>
<div class="line"><a name="l01020"></a><span class="lineno"> 1020</span>&#160; </div>
<div class="line"><a name="l01021"></a><span class="lineno"> 1021</span>&#160;<span class="preprocessor">#endif </span><span class="comment">// EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_H</span></div>
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